CN107749054B - Image processing method, device and storage medium - Google Patents
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Abstract
The embodiment of the invention provides an image processing method, which comprises the following steps: acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information; performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information; and obtaining a first target image based on the first target low-frequency information and the second target high-frequency information. The embodiment of the invention also provides an image processing device and a storage medium. According to the embodiment of the invention, the noise reduction processing can be performed on the high-frequency information based on the image in a targeted manner in a short time, so that the noise reduction processing efficiency is improved.
Description
Technical Field
The present invention relates to image processing technologies, and in particular, to an image processing method, an image processing apparatus, and a storage medium.
Background
In the related art, when an image is processed, the image is processed in a time domain or a frequency domain, not only is the processing time long, but also noise in the image cannot be processed in a targeted manner, and therefore, the noise reduction effect on the image is not ideal.
Disclosure of Invention
In view of the above, embodiments of the present invention provide an image processing method, an image processing apparatus, and a storage medium to solve the problems in the prior art.
The embodiment of the invention provides an image processing method, which comprises the following steps:
acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information;
performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information;
and obtaining a first target image based on the first target low-frequency information and the second target high-frequency information.
In the foregoing solution, the acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information includes:
and performing downsampling processing on the first initial image to acquire low-frequency information of an image with a preset proportion in the first initial image and high-frequency information corresponding to the low-frequency information of the image with the preset proportion.
In the foregoing solution, the acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information includes:
performing downsampling processing on the first initial image to acquire low-frequency information of a quarter image in the first initial image and high-frequency information of the first initial image;
performing downsampling processing on the quarter image to acquire low-frequency information of a sixteenth image in the first initial image and high-frequency information of the quarter image in the first initial image;
performing downsampling processing on the sixteenth image to acquire low-frequency information of a sixtieth fourth image in the first initial image and high-frequency information of a sixtieth image in the first initial image;
determining low-frequency information of a quarter image in the first initial image, low-frequency information of a sixteenth image in the first initial image and low-frequency information of a sixteenth image in the first initial image as first target low-frequency information, and determining high-frequency information of the first initial image, high-frequency information of a quarter image in the first initial image and high-frequency information of a sixteenth image in the first initial image as first target high-frequency information.
In the foregoing solution, the performing, on the basis of image features of different dimensions of the first target image, first noise reduction processing on the first target high-frequency information includes:
combining the first target low-frequency information with first target high-frequency information corresponding to the first target low-frequency information to obtain a first transformation image;
determining first image features of different dimensions of the first transformed image;
and configuring a characteristic value corresponding to the first image characteristic based on the first image characteristic to obtain second target high-frequency information.
In the foregoing solution, before the obtaining of the second target high-frequency information, the method further includes:
acquiring second target low-frequency information of a second initial image and third target high-frequency information corresponding to the second target low-frequency information; the second initial image represents the same object as the first initial image, and the object state of the second initial image is different from the object state of the first initial image.
In the foregoing solution, the obtaining a first target image based on the first target low-frequency information and the second target high-frequency information includes:
acquiring a first image matrix representing the first initial image and a second image matrix representing the second initial image;
determining the matching degree of the first target high-frequency information and the third target high-frequency information based on the first image matrix and the second image matrix;
determining a first weight corresponding to the first target high-frequency information and a second weight corresponding to the third target high-frequency information based on the matching degree;
fusing the first target low-frequency information and the second target low-frequency information based on the first weight and the second weight to obtain third target low-frequency information;
and updating the first target low-frequency information by using the third target low-frequency information.
In the above scheme, the method further comprises:
and fusing the second target high-frequency information and the third target high-frequency information based on the first weight and the second weight to obtain fourth target high-frequency information.
In the foregoing solution, the obtaining a first target image based on the first target low-frequency information and the second target high-frequency information includes:
updating the second target high frequency information with the fourth target high frequency information;
and combining the updated second target high-frequency information with the corresponding updated first target low-frequency information to obtain a first target image.
An embodiment of the present invention further provides an image processing apparatus, where obtaining a first target image based on the first target low-frequency information and the second target high-frequency information includes:
updating the second target high frequency information with the fourth target high frequency information;
and combining the updated second target high-frequency information with the corresponding updated first target low-frequency information to obtain a first target image.
An embodiment of the present invention further provides a storage medium, where the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to implement the following steps:
acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information;
performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information;
and obtaining a first target image based on the first target low-frequency information and the second target high-frequency information.
In the embodiment of the invention, the plurality of high-frequency information and the plurality of low-frequency information of the image to be processed are acquired, and the noise in the image can be processed in a targeted manner by carrying out noise reduction processing on the plurality of high-frequency information.
And anisotropic diffusion denoising is carried out through multiple acquired high-frequency information, so that the denoising effect of the image is further improved.
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Fig. 1 is a schematic diagram of a hardware structure of a mobile terminal according to the present invention;
FIG. 2 is a diagram of a communication network system architecture according to the present invention;
FIG. 3 is a schematic diagram of a basic processing flow of an image processing method according to an embodiment of the present invention;
FIG. 4 is a schematic view of a processing flow for acquiring high-frequency information and low-frequency information of a first target according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a process of obtaining a first target image according to an embodiment of the present invention;
FIG. 6 is a schematic processing flow chart of a second image processing method according to the embodiment of the present invention;
FIG. 7 is a flowchart illustrating a process of obtaining a first target image according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
In the embodiment of the present invention, the image processing apparatus for implementing the image processing method may be any electronic device, such as a server, a terminal, and the like; the image to be processed may be stored in a storage area of the electronic device itself, or may be transmitted to the electronic device by another device through a network.
When the electronic device is a terminal, the terminal may be implemented in various forms. For example, the terminal described in the present invention may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a wearable device, a smart band, a pedometer, and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like.
The following description will be given by way of example of a mobile terminal, and it will be understood by those skilled in the art that the construction according to the embodiment of the present invention can be applied to a fixed type terminal, in addition to elements particularly used for mobile purposes.
Referring to fig. 1, which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present invention, the mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, a/V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 1 is not intended to be limiting of mobile terminals, which may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile terminal in detail with reference to fig. 1:
the Radio Frequency unit 101 may be configured to receive and transmit signals during a message transmission or call, specifically, receive downlink information of a base station and then process the received downlink information to the processor 110, and transmit uplink data to the base station, in General, the Radio Frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like, and in addition, the Radio Frequency unit 101 may further communicate with a network and other devices through wireless communication, and the wireless communication may use any communication standard or protocol, including, but not limited to, GSM (Global System of Mobile communication), GPRS (General Packet Radio Service), CDMA2000(Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access ), TD-SCDMA (Synchronous Time Division Multiple Access, Code Division Multiple Access, Time Division Multiple Access, etc., TDD — Time Division Multiple Access, L Time Division Multiple Access, etc.
WiFi belongs to short-distance wireless transmission technology, and the mobile terminal can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 102, and provides wireless broadband internet access for the user. Although fig. 1 shows the WiFi module 102, it is understood that it does not belong to the essential constitution of the mobile terminal, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The audio output unit 103 may convert audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output as sound when the mobile terminal 100 is in a call signal reception mode, a call mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 103 may also provide audio output related to a specific function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, and the like.
The a/V input unit 104 is used to receive audio or video signals. The a/V input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, the Graphics processor 1041 Processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphic processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 may receive sound (audio data) via the microphone 1042 in an operation mode such as a phone call mode, a recording mode, a voice recognition mode, and the like, and can process such sound into audio data. The processed audio (voice) data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 101 in case of a phone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.
The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 1061 and/or a backlight when the mobile terminal 100 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.
The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in the form of a liquid Crystal Display (L acquired Crystal Display, L CD), an Organic light-Emitting Diode (O L ED), or the like.
The user input unit 107 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect a touch operation performed by a user on or near the touch panel 1071 (e.g., an operation performed by the user on or near the touch panel 1071 using a finger, a stylus, or any other suitable object or accessory), and drive a corresponding connection device according to a predetermined program. The touch panel 1071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may include other input devices 1072. In particular, other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.
Further, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the processor 110 to determine the type of the touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although the touch panel 1071 and the display panel 1061 are shown in fig. 1 as two separate components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions of the mobile terminal, and is not limited herein.
The interface unit 108 serves as an interface through which at least one external device is connected to the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and external devices.
The memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 109 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 110 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 109 and calling data stored in the memory 109, thereby performing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The mobile terminal 100 may further include a power supply 111 (e.g., a battery) for supplying power to various components, and preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, so as to manage charging, discharging, and power consumption management functions via the power management system.
Although not shown in fig. 1, the mobile terminal 100 may further include a bluetooth module or the like, which is not described in detail herein.
In order to facilitate understanding of the embodiments of the present invention, a communication network system on which the mobile terminal of the present invention is based is described below.
Referring to fig. 2, fig. 2 is an architecture diagram of a communication Network system according to an embodiment of the present invention, the communication Network system is L TE system of universal mobile telecommunications technology, and the L TE system includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203, and an IP service 204 of an operator, which are in communication connection in sequence.
Specifically, the UE201 may be the terminal 100 described above, and is not described herein again.
The E-UTRAN202 includes eNodeB2021 and other eNodeBs 2022, among others. Among them, the eNodeB2021 may be connected with other eNodeB2022 through backhaul (e.g., X2 interface), the eNodeB2021 is connected to the EPC203, and the eNodeB2021 may provide the UE201 access to the EPC 203.
The EPC203 may include an MME (Mobility Management Entity) 2031, an HSS (Home Subscriber Server) 2032, other MMEs 2033, an SGW (Serving gateway) 2034, a PGW (PDN gateway) 2035, and a PCRF (Policy and charging functions Entity) 2036, and the like. The MME2031 is a control node that handles signaling between the UE201 and the EPC203, and provides bearer and connection management. HSS2032 is used to provide registers to manage functions such as home location register (not shown) and holds subscriber specific information about service characteristics, data rates, etc. All user data may be sent through SGW2034, PGW2035 may provide IP address assignment for UE201 and other functions, and PCRF2036 is a policy and charging control policy decision point for traffic data flow and IP bearer resources, which selects and provides available policy and charging control decisions for a policy and charging enforcement function (not shown).
The IP services 204 may include the internet, intranets, IMS (IP Multimedia Subsystem), or other IP services, among others.
Although L TE system is described as an example, it should be understood by those skilled in the art that the present invention is not limited to L TE system, but also applicable to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems.
Based on the above mobile terminal hardware structure and communication network system, the present invention provides various embodiments of the method.
Example one
A processing flow diagram of an image processing method according to an embodiment of the present invention, as shown in fig. 3, includes the following steps:
step S101, acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information.
In an alternative embodiment, as shown in fig. 4, the image processing apparatus performs downsampling processing on the first initial image to obtain low-frequency information of a quarter image in the first initial image and high-frequency information of the first initial image; then, the quarter images are subjected to the same downsampling processing, and low-frequency information of one sixteenth image in the first initial image and high-frequency information of one quarter image in the first initial image are obtained; the sixteenth image is subjected to the same downsampling processing, and low-frequency information of a sixtieth quarter image in the first initial image and high-frequency information of a sixtieth image in the first initial image are obtained; determining low-frequency information of a quarter image in the first initial image, low-frequency information of a sixteenth image in the first initial image and low-frequency information of a sixteenth image in the first initial image as first target low-frequency information, and determining high-frequency information of the first initial image, high-frequency information of a quarter image in the first initial image and high-frequency information of a sixteenth image in the first initial image as first target high-frequency information.
Of course, based on the above alternative embodiment, the same downsampling process may be further performed on the sixty-fourth image in the first initial image to obtain the low-frequency information of the two-hundred and fifty-sixth image in the first initial image and the high-frequency information of the sixty-fourth image in the first initial image; and repeating the steps until the preset amount of high-frequency information is obtained.
And S102, performing first noise reduction processing on the first target high-frequency information based on the image features with different dimensions to obtain second target high-frequency information.
Specifically, the image processing apparatus first combines the first target low-frequency information with first target high-frequency information corresponding to the first target low-frequency information to obtain a first transformed image. Then, performing anisotropic diffusion denoising based on image features, specifically, determining first image features of different dimensions of the first transformed image; configuring a characteristic value corresponding to the first image characteristic based on the first image characteristic to obtain second target high-frequency information; therefore, the denoising processing of the high-frequency information is realized, and the edge information of the image is reserved.
In an alternative embodiment, the image processing means combines the low frequency information of the sixty-fourth image of the first initial image with the high frequency information of the sixty-fourth image of the first initial image to obtain a first transformed image; using coherence to determine first image features for different dimensions of the image,
such as edge regions, smooth regions, T-corner regions, etc.; the eigenvalue of the region diffusion tensor corresponding to the edge region is arranged based on the determined edge region, the eigenvalue of the region diffusion tensor corresponding to the smooth region is arranged based on the determined smooth region, and the eigenvalue of the region diffusion tensor corresponding to the T-shaped corner region is arranged based on the determined T-shaped corner region.
The image processing device combines the low-frequency information of one-sixteenth image in the first initial image with the high-frequency information of one-fourth image in the first initial image to obtain another first transformation image; utilizing coherence to determine first image characteristics of different dimensions of the image, such as an edge area, a smooth area, a T-shaped corner area and the like; the eigenvalue of the region diffusion tensor corresponding to the edge region is arranged based on the determined edge region, the eigenvalue of the region diffusion tensor corresponding to the smooth region is arranged based on the determined smooth region, and the eigenvalue of the region diffusion tensor corresponding to the T-shaped corner region is arranged based on the determined T-shaped corner region.
The image processing device combines the low-frequency information of the quarter image in the first initial image with the high-frequency information of the first initial image to obtain a second transformed image; utilizing coherence to determine first image characteristics of different dimensions of the image, such as an edge area, a smooth area, a T-shaped corner area and the like; the eigenvalue of the region diffusion tensor corresponding to the edge region is arranged based on the determined edge region, the eigenvalue of the region diffusion tensor corresponding to the smooth region is arranged based on the determined smooth region, and the eigenvalue of the region diffusion tensor corresponding to the T-shaped corner region is arranged based on the determined T-shaped corner region. Through the configuration of the characteristic values, the noise reduction processing of the first target high-frequency information is realized, and the obtained second target high-frequency information comprises the following steps: the high-frequency information of the first initial image subjected to the noise reduction processing, the high-frequency information of a quarter image in the first initial image subjected to the noise reduction processing, and the high-frequency information of a sixteenth image in the first initial image subjected to the noise reduction processing.
An alternative embodiment for determining image features using coherence is as follows:
defining structure tensor
Wherein u isw＝wiener2(u)，
The gradient is indicated. Tensor of structure
Is represented by λ1And λ1Wherein λ is1>λ2；
Here, the eigenvalues λ of the structure tensor1、λ2The image structure information is included:
1) if λ1≈λ2And 0, which represents the feature that the gray scale variation of the image in any direction around this point is small, i.e., the smooth area of the image.
2) If λ1>>λ2And the value is approximately equal to 0, the change rate of the image along a certain direction is far larger than that along the direction vertical to the direction, and the image is characterized by a Hovered line structure with obvious edges.
3) If λ1≈λ2>>0, which means that the gray scale of the image changes rapidly in two mutually perpendicular directions, is the representation of the existence of corners or T-shaped local structures of the image.
We define the image coherence as H ═ λ (λ)1-λ2)2＝(C11-C22)2+4C12 2；
Therefore, the image features can be classified using the coherence H as follows:
smooth area: lambda [ alpha ]1≈λ2≈0,H≈0
Edge or streamline structure: lambda [ alpha ]1>>λ2≈0,H>>0
Corner or T-shaped partial structure: lambda [ alpha ]1≈λ2>>0,H≈0
From the above division, two thresholds can be set, the first threshold threth1 is a constant very close to 0 and greater than 0 to distinguish image features, the second threshold threth2 is a constant much greater than 0, and when H is greater than or equal to thresh1, it is determined as an edge; otherwise smooth areas or corners. When H is greater than or equal to thresh1 and lambda2>thresh2, judged as a corner or Tx shaped partial structure; otherwise, it is a smooth area.
Step S103, obtaining a first target image based on the first target low-frequency information and the second target high-frequency information.
The image processing device combines the first target low-frequency information with the corresponding first target high-frequency information subjected to noise reduction processing to obtain a first target image.
In an alternative embodiment, as shown in fig. 5, the image processing apparatus combines the high frequency information of the sixteenth image in the first initial image subjected to the noise reduction processing with the low frequency information of the sixteenth image in the first initial image to obtain the sixteenth image in the first target image. In a preferred embodiment, before obtaining the sixteenth image in the first initial image, noise reduction processing may be performed on low-frequency information of the sixteenth image in the first initial image, and then the low-frequency information and the high-frequency information of the sixteenth image in the first initial image subjected to the noise reduction processing are combined to obtain the sixteenth image in the first target image.
The image processing device combines the low-frequency information of the sixteenth image in the first target image with the high-frequency information of the quarter image in the first initial image subjected to noise reduction processing to obtain the quarter image in the first target image.
The image processing device combines the low-frequency information of the quarter image in the first target image with the high-frequency information of the first initial image subjected to noise reduction processing to obtain a first target image.
Example two
As shown in fig. 6, a processing flow of the image processing method according to the second embodiment of the present invention includes the following steps:
in step S201, the information processing apparatus acquires first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information.
In step S202, the information processing apparatus acquires second target low-frequency information of a second initial image and third target high-frequency information corresponding to the third target low-frequency information.
The implementation process of the image processing apparatus executing steps S201 and S202 is the same as the implementation process of step S101, and is not described here again.
In an embodiment of the present invention, the second initial image and the first initial image represent the same object, and the object state of the second initial image is different from the object state of the first initial object. It is understood that the first initial image includes a motion region, that is, the object state of the first initial image changes with time, and the second initial image is formed.
In an optional embodiment, the image processing device performs registration alignment on first low-frequency information of a first initial image and second low-frequency information of a second initial image by using an L ucas-Kanade L K optical flow method, detects whether an object state of the first initial object is the same as that of the second initial object, considers that no motion area exists in the first initial image when the object state of the first initial object is the same as that of the second initial object, considers that a motion area exists in the first initial object when the object state of the initial object is different from that of the second initial object, and processes the first initial image, namely processes the first initial image and the second initial image and then fuses the first initial image and the second initial image into a target image.
In step S203, the information processing apparatus performs first noise reduction processing on the first target high-frequency information based on the image features of different dimensions to obtain second target high-frequency information.
The image processing apparatus performs the implementation of step S203, which is the same as the implementation of step S103, and is not described here again.
In step S204, the information processing apparatus obtains a first target image based on the first target low-frequency information and the second target high-frequency information.
In an alternative embodiment, the processing procedure of the image processing apparatus to obtain the first target image, as shown in fig. 7, includes the following steps:
step S2041, a first image matrix representing the first initial image and a second image matrix representing the second initial image are obtained.
Step S2042, determining a matching degree between the first target high frequency information and the third target high frequency information based on the first image matrix and the second image matrix.
Specifically, a conversion matrix from the first image to the second image is calculated by the RANSAC method based on the first image matrix and the second image matrix. And searching matched pixel points of the pixel points on the first image on the second image, and counting the matching degree of the area blocks corresponding to the pixel points by utilizing the difference degree of the pixel points obtained by converting the matrix of the first image.
Step S2043, determining a first weight corresponding to the first target high frequency information and a second weight corresponding to the third target high frequency information based on the matching degree.
Specifically, when the matching degree is high, the corresponding weight value is large, and when the matching degree is low, the corresponding weight value is small.
Step S2044, based on the first weight and the second weight, fusing the first target low-frequency information and the second target low-frequency information to obtain third target low-frequency information.
Step S2045, updating the first target low frequency information by using the third target low frequency information.
Step S2046, based on the first target low frequency information and the second target high frequency information, a first target image is obtained.
Here, the first target low frequency information is updated first target low frequency information, and the updated first target low frequency information is equal to the third target low frequency information.
In an alternative embodiment, the information processing apparatus updates the second target high frequency information with the third target high frequency information when a preset condition is satisfied. Here, the preset condition includes at least one of: the intensity of the third target high-frequency information is greater than that of the second target high-frequency information, and the generation time of the second initial image corresponding to the third target high-frequency information is later than that of the first initial image corresponding to the second target high-frequency information. At this time, it may be understood that the first target image is obtained based on the third target low-frequency information and the second target high-frequency information. The implementation process of obtaining the first target image based on the third target low-frequency information and the second target high-frequency information is the same as the implementation process of step S103, and is not repeated here.
In another optional embodiment, the information processing apparatus fuses the second target high frequency information and the third target high frequency information based on the first weight and the second weight to obtain fourth target high frequency information. For example, the first weight is X, the second weight is Y, and then the fourth target high-frequency information is X × the second target high-frequency information + Y × the third target high-frequency information. And updating the second target high frequency information by using the fourth target high frequency information. At this time, it can be understood that the first target image is obtained based on the fourth target high frequency information and the third target low frequency information. The implementation process of obtaining the first target image based on the fourth target high-frequency information and the third target low-frequency information is the same as the implementation process of step S103, and is not repeated here.
EXAMPLE III
In order to implement the foregoing image processing method embodiment of the present invention, a third embodiment of the present invention further provides an image processing apparatus, where a schematic structural diagram of the image processing apparatus, as shown in fig. 8, includes: a processor 301, a memory 302, and a communication bus 303; wherein,
the communication bus 303 is used for realizing connection communication between the processor 301 and the memory 302;
the processor 301 is configured to execute the image processing program stored in the memory 302 to implement the following steps:
acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information;
performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information;
and obtaining a first target image based on the first target low-frequency information and the second target high-frequency information.
In a preferred embodiment, the processor 301 is specifically configured to perform downsampling on the first initial image to obtain low-frequency information of an image with a preset ratio in the first initial image and high-frequency information corresponding to the low-frequency information of the image with the preset ratio.
In a preferred embodiment, the processor 301 is specifically configured to perform downsampling on the first initial image to obtain low-frequency information of a quarter image in the first initial image and high-frequency information of the first initial image;
performing downsampling processing on the quarter image to acquire low-frequency information of a sixteenth image in the first initial image and high-frequency information of the quarter image in the first initial image;
performing downsampling processing on the sixteenth image to acquire low-frequency information of a sixtieth fourth image in the first initial image and high-frequency information of a sixtieth image in the first initial image;
determining low-frequency information of a quarter image in the first initial image, low-frequency information of a sixteenth image in the first initial image and low-frequency information of a sixteenth image in the first initial image as first target low-frequency information, and determining high-frequency information of the first initial image, high-frequency information of a quarter image in the first initial image and high-frequency information of a sixteenth image in the first initial image as first target high-frequency information.
In a preferred embodiment, the processor 301 is specifically configured to combine the first target low-frequency information with first target high-frequency information corresponding to the first target low-frequency information to obtain a first transformed image;
determining first image features of different dimensions of the first transformed image;
and configuring a characteristic value corresponding to the first image characteristic based on the first image characteristic to obtain second target high-frequency information.
In a preferred embodiment, the processor 301 is further configured to obtain second target low-frequency information of a second initial image and third target high-frequency information corresponding to the second target low-frequency information; the second initial image represents the same object as the first initial image, and the object state of the second initial image is different from the object state of the first initial image.
In a preferred embodiment, the processor 301 is specifically configured to obtain a first image matrix representing the first initial image and a second image matrix representing the second initial image;
determining the matching degree of the first target high-frequency information and the third target high-frequency information based on the first image matrix and the second image matrix;
determining a first weight corresponding to the first target high-frequency information and a second weight corresponding to the third target high-frequency information based on the matching degree;
fusing the first target low-frequency information and the second target low-frequency information based on the first weight and the second weight to obtain third target low-frequency information;
and updating the first target low-frequency information by using the third target low-frequency information.
In a preferred embodiment, the processor 301 is further configured to fuse the second target high-frequency information and the third target high-frequency information based on the first weight and the second weight to obtain fourth target high-frequency information.
In a preferred embodiment, the processor 301 is specifically configured to update the second target high frequency information with the fourth target high frequency information;
and combining the updated second target high-frequency information with the corresponding updated first target low-frequency information to obtain a first target image.
In the embodiment of the present invention, the functions executed by the processor 301 in the image processing apparatus may be implemented by a Central Processing Unit (CPU), a microprocessor unit (MPU), a Digital Signal Processor (DSP), or a programmable gate array (FPGA) located on the same electronic device.
Example four
To achieve the foregoing image processing apparatus and method embodiments of the present invention, a fourth embodiment of the present invention further provides a storage medium, where the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to implement the following steps:
acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information;
performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information;
and obtaining a first target image based on the first target low-frequency information and the second target high-frequency information.
In a preferred embodiment, the one or more programs are specifically executable by the one or more processors to perform the steps of:
and performing downsampling processing on the first initial image to acquire low-frequency information of an image with a preset proportion in the first initial image and high-frequency information corresponding to the low-frequency information of the image with the preset proportion.
In a preferred embodiment, the one or more programs are specifically executable by the one or more processors to perform the steps of:
performing downsampling processing on the first initial image to acquire low-frequency information of a quarter image in the first initial image and high-frequency information of the first initial image;
performing downsampling processing on the quarter image to acquire low-frequency information of a sixteenth image in the first initial image and high-frequency information of the quarter image in the first initial image;
performing downsampling processing on the sixteenth image to acquire low-frequency information of a sixtieth fourth image in the first initial image and high-frequency information of a sixtieth image in the first initial image;
determining low-frequency information of a quarter image in the first initial image, low-frequency information of a sixteenth image in the first initial image and low-frequency information of a sixteenth image in the first initial image as first target low-frequency information, and determining high-frequency information of the first initial image, high-frequency information of a quarter image in the first initial image and high-frequency information of a sixteenth image in the first initial image as first target high-frequency information.
In a preferred embodiment, the one or more programs are specifically executable by the one or more processors to perform the steps of:
combining the first target low-frequency information with first target high-frequency information corresponding to the first target low-frequency information to obtain a first transformation image;
determining first image features of different dimensions of the first transformed image;
and configuring a characteristic value corresponding to the first image characteristic based on the first image characteristic to obtain second target high-frequency information.
In a preferred embodiment, the one or more programs are further executable by the one or more processors to perform the steps of:
acquiring second target low-frequency information of a second initial image and third target high-frequency information corresponding to the second target low-frequency information; the second initial image represents the same object as the first initial image, and the object state of the second initial image is different from the object state of the first initial image.
In a preferred embodiment, the one or more programs are specifically executable by the one or more processors to perform the steps of:
acquiring a first image matrix representing the first initial image and a second image matrix representing the second initial image;
determining the matching degree of the first target high-frequency information and the third target high-frequency information based on the first image matrix and the second image matrix;
determining a first weight corresponding to the first target high-frequency information and a second weight corresponding to the third target high-frequency information based on the matching degree;
fusing the first target low-frequency information and the second target low-frequency information based on the first weight and the second weight to obtain third target low-frequency information;
and updating the first target low-frequency information by using the third target low-frequency information.
In a preferred embodiment, the one or more programs are further executable by the one or more processors to perform the steps of:
and fusing the second target high-frequency information and the third target high-frequency information based on the first weight and the second weight to obtain fourth target high-frequency information.
In a preferred embodiment, the one or more programs are specifically executable by the one or more processors to perform the steps of:
updating the second target high frequency information with the fourth target high frequency information;
and combining the updated second target high-frequency information with the corresponding updated first target low-frequency information to obtain a first target image.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. An image processing method, comprising:
acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information;
performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information;
obtaining a first target image based on the first target low-frequency information and the second target high-frequency information;
the obtaining a first target image based on the first target low-frequency information and the second target high-frequency information comprises:
acquiring a first image matrix representing the first initial image and a second image matrix representing the second initial image;
acquiring second target low-frequency information of the second initial image and third target high-frequency information corresponding to the second target low-frequency information;
determining the matching degree of the first target high-frequency information and the third target high-frequency information based on the first image matrix and the second image matrix;
determining a first weight corresponding to the first target high-frequency information and a second weight corresponding to the third target high-frequency information based on the matching degree;
fusing the first target low-frequency information and the second target low-frequency information based on the first weight and the second weight to obtain third target low-frequency information;
updating the first target low-frequency information by using the third target low-frequency information;
wherein the second initial image represents the same object as the first initial image, and the object state of the second initial image is different from the object state of the first initial image.
2. The method of claim 1, wherein the acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information comprises:
and performing downsampling processing on the first initial image to acquire low-frequency information of an image with a preset proportion in the first initial image and high-frequency information corresponding to the low-frequency information of the image with the preset proportion.
3. The method of claim 2, wherein the acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information comprises:
performing downsampling processing on the first initial image to acquire low-frequency information of a quarter image in the first initial image and high-frequency information of the first initial image;
performing downsampling processing on the quarter image to acquire low-frequency information of a sixteenth image in the first initial image and high-frequency information of the quarter image in the first initial image;
performing downsampling processing on the sixteenth image to acquire low-frequency information of a sixtieth fourth image in the first initial image and high-frequency information of a sixtieth image in the first initial image;
determining low-frequency information of a quarter image in the first initial image, low-frequency information of a sixteenth image in the first initial image and low-frequency information of a sixteenth image in the first initial image as first target low-frequency information, and determining high-frequency information of the first initial image, high-frequency information of a quarter image in the first initial image and high-frequency information of a sixteenth image in the first initial image as first target high-frequency information.
4. The method according to claim 1, wherein the performing a first noise reduction process on the first target high-frequency information based on image features of different dimensions of the first target image comprises:
combining the first target low-frequency information with first target high-frequency information corresponding to the first target low-frequency information to obtain a first transformation image;
determining first image features of different dimensions of the first transformed image;
and configuring a characteristic value corresponding to the first image characteristic based on the first image characteristic to obtain second target high-frequency information.
5. The method according to any one of claims 1-4, further comprising:
and fusing the second target high-frequency information and the third target high-frequency information based on the first weight and the second weight to obtain fourth target high-frequency information.
6. The method of claim 5, wherein obtaining a first target image based on the first target low frequency information and the second target high frequency information comprises:
updating the second target high frequency information with the fourth target high frequency information;
and combining the updated second target high-frequency information with the corresponding updated first target low-frequency information to obtain a first target image.
7. An image processing apparatus characterized by comprising: a processor, a memory, and a communication bus;
the communication bus is used for realizing connection communication between the processor and the memory;
the processor is used for executing the image processing program stored in the memory so as to realize the following steps:
acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information;
performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information;
obtaining a first target image based on the first target low-frequency information and the second target high-frequency information;
the obtaining a first target image based on the first target low-frequency information and the second target high-frequency information comprises:
acquiring a first image matrix representing the first initial image and a second image matrix representing the second initial image;
acquiring second target low-frequency information of the second initial image and third target high-frequency information corresponding to the second target low-frequency information;
determining the matching degree of the first target high-frequency information and the third target high-frequency information based on the first image matrix and the second image matrix;
determining a first weight corresponding to the first target high-frequency information and a second weight corresponding to the third target high-frequency information based on the matching degree;
fusing the first target low-frequency information and the second target low-frequency information based on the first weight and the second weight to obtain third target low-frequency information;
updating the first target low-frequency information by using the third target low-frequency information;
wherein the second initial image represents the same object as the first initial image, and the object state of the second initial image is different from the object state of the first initial image.
8. A storage medium storing one or more programs, the one or more programs executable by one or more processors to perform the steps of:
acquiring first target low-frequency information of a first initial image and first target high-frequency information corresponding to the first target low-frequency information;
performing first noise reduction processing on the first target high-frequency information based on image features of different dimensions to obtain second target high-frequency information;
obtaining a first target image based on the first target low-frequency information and the second target high-frequency information;
the obtaining a first target image based on the first target low-frequency information and the second target high-frequency information comprises:
acquiring a first image matrix representing the first initial image and a second image matrix representing the second initial image;
acquiring second target low-frequency information of a second initial image and third target high-frequency information corresponding to the second target low-frequency information;
determining the matching degree of the first target high-frequency information and third target high-frequency information based on the first image matrix and the second image matrix;
determining a first weight corresponding to the first target high-frequency information and a second weight corresponding to the third target high-frequency information based on the matching degree;
fusing the first target low-frequency information and the second target low-frequency information based on the first weight and the second weight to obtain third target low-frequency information;
updating the first target low-frequency information by using the third target low-frequency information;
wherein the second initial image represents the same object as the first initial image, and the object state of the second initial image is different from the object state of the first initial image.
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