WO2020107995A1 - Imaging method and apparatus, electronic device, and computer readable storage medium - Google Patents

Abstract

An imaging method comprises: acquiring a first image; converting the first image, by means of a color space, into a second image comprising a luminance channel; and acquiring a point spread function, and using the point spread function to perform inverse point spread function transformation of the second image, to obtain a target image.

Description

Imaging method and device, electronic equipment, and computer-readable storage medium

Cross-reference of related applications

This application requires the priority of the Chinese patent application filed on November 26, 2018 in the Chinese Patent Office with the application number 2018114193949 and the invention titled "Imaging method and device, electronic equipment, computer-readable storage medium" The reference is incorporated in this application.

Technical field

The present application relates to the field of imaging technology, and in particular, to an imaging method, device, electronic device, and computer-readable storage medium.

Background technique

With the development of optical imaging technology, various electronic devices with optical imaging systems have appeared. The optical imaging system in the electronic device projects the subject onto the photosensitive surface of the sensor, and obtains the RGB (Red Green Blue, red, green, blue) three-channel response value through the photoelectric effect. The RGB three-channel response value is then subjected to autofocus, automatic exposure, After automatic white balance and color space conversion, the final image is obtained. However, traditional imaging methods are affected by aberrations, resulting in blurred images.

Summary of the invention

Embodiments of the present application provide an imaging method, apparatus, electronic device, and computer-readable storage medium.

An imaging method, including:

Get the first image;

Converting the first image into a second image containing a brightness channel through a color space;

Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image.

An imaging device, including:

The image acquisition module is used to acquire the first image;

An image conversion module, configured to convert the first image into a second image including a luminance channel through a color space;

An inverse transformation module is used to obtain a point spread function, and perform an inverse point spread function transformation on the second image by using the point spread function to obtain a target image.

An electronic device includes a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor is caused to perform the following steps:

Get the first image;

Converting the first image into a second image containing a brightness channel through a color space;

Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image.

A computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are realized:

Get the first image;

Converting the first image into a second image containing a brightness channel through a color space;

Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image.

The imaging method, apparatus, electronic device, and computer-readable storage medium in this embodiment convert the acquired first image into a second image containing a luminance channel through a color space, and then acquire a point spread function to inverse the second image Point diffusion function conversion to obtain the target image, the image inverse point diffusion function conversion, effectively remove the image edge blur, improve the image clarity.

BRIEF DESCRIPTION

In order to more clearly explain the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present application. For those of ordinary skill in the art, without paying any creative work, other drawings can be obtained based on these drawings.

FIG. 1 is an application environment diagram of an imaging method in an embodiment;

2 is a flowchart of an imaging method in an embodiment;

FIG. 3 is a schematic diagram of the principle that an original image undergoes diffusion to form a circle of confusion in an embodiment;

4 is a flowchart of an imaging method in another embodiment;

5 is a structural block diagram of an imaging device in an embodiment;

6 is a structural block diagram of an imaging device in another embodiment;

7 is a block diagram of an internal structure of an electronic device in an embodiment;

8 is a schematic diagram of an image processing circuit in an embodiment.

detailed description

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be described in further detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

It can be understood that the terms “first”, “second”, etc. used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of the present application, the first image may be referred to as the second image, and similarly, the second image may be referred to as the first image. Both the first image and the second image are images, but they are not the same image.

FIG. 1 is a schematic diagram of an application environment of an imaging method in an embodiment. As shown in FIG. 1, the application environment includes an electronic device 110. An electronic imaging system is installed on the electronic device 110, and an object is photographed through the optical imaging system and projected onto the photosensitive surface of the image sensor, and the image sensor outputs RGB (Red Green Blue) values through the photoelectric effect. After the RGB value is corrected by autofocus, auto white balance, and demosaicing algorithm, an RGB image with normal focus, brightness and color that meets human perception is obtained. The RGB image is converted into a color space conversion matrix to obtain a YUV image, and the YUV image is inverted Point spread function transformation to get the target image. "Y" in YUV images means brightness (Luminance or Luma), that is, grayscale value; "U" and "V" mean chroma (Chrominance or Chroma), the role is to describe the image color and saturation, use The color of the specified pixel. The inverse point spread function transformation is performed through the YUV image, which effectively removes the image edge blurring and improves the image clarity. Among them, the electronic device 110 may be a smart phone with an optical imaging system such as a camera, a personal computer, a tablet computer, a personal digital assistant, a camera, a wearable device, and the like.

2 is a flowchart of an imaging method in an embodiment. The imaging method in this embodiment is described by taking the electronic device 110 in FIG. 1 as an example. As shown in FIG. 2, the imaging method includes steps 202 to 206.

Step 202: Acquire the first image.

The first image may be an RGB image.

Specifically, the electronic device images the subject through an optical imaging system (such as a lens) and projects it onto the photosensitive surface of the image sensor. The image sensor outputs RGB three-channel response values through the photoelectric effect. RGB images are obtained after processing such as exposure and automatic white balance.

Step 204: Convert the first image into a second image containing a luminance channel through a color space.

Among them, the color space conversion means that the image is converted from one color space to another color space. The second image is an image containing a luminance channel. The brightness channel refers to the brightness signal component contained in the image format.

Specifically, the electronic device converts the first image into a second image containing a brightness channel through a color space. The second image may be a YUV image, HSV image, YCrCb image, YIQ image, HIS image, or the like. In HSV, H represents Hue, S represents saturation, and V represents brightness. In YCrCb, Y represents brightness (Luminance or Luma), Cr represents hue, and Cb represents saturation. In YIQ, Y stands for Luminance brightness, I stands for In-phase, the color changes from orange to cyan, and Q stands for Quadrature-phase. In HIS, H means Hue, S means Saturation, and I means Luminance.

Optionally, the electronic device converts the RGB image into a YUV image through the color space.

Step 206: Obtain a point spread function, and use the point spread function to perform inverse point spread function transformation on the second image to obtain a target image.

Among them, after passing through the optical system, the point light source will form an expanded circle of confusion due to aberration and diffraction, which can be described by a point spread function (Point Spread Function, PSF). Image edge blurring can be regarded as a convolution process of the two-dimensional point spread function and the original image. As shown in Fig. 3, the diffused blurred image is calculated using f(x, y) using the following formula (1).

f(x,y)=∫∫PSF(α,β)g(x-α,y-β)dαdβ+n(x,y) Formula (1)

Where g(x,y) is the original image data, PSF(α,β) is the point spread function, n(x,y) is the noise, the value of α is 0 to x, and the value of β is 0 to y . (x, y) is the coordinate value of pixels in the image.

Taking the Fourier changes on both sides of formula (1), formula (2) can be obtained

F(u,v)=PSF(u,v)G(u,v)+N(u,v) Formula (2)

For the approximate processing of limited noise, the recovered image after removing the PSF function can be obtained, as shown in formula (3).

Inverse point spread function transformation refers to dividing the pixels in the image by the point spread function, or dividing the pixels in the image by the point spread function, and then multiplying by the coefficient. The coefficient may be 0.5, 0.6, 0.9, 1, 1.1, etc., not limited to this.

Specifically, the electronic device obtains a point spread function, and uses the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image.

In the imaging method in this embodiment, the acquired first image is converted into a second image containing a brightness channel through a color space, and then a point spread function is acquired, and the second image is converted into an inverse point spread function to obtain a target image. The inverse point spread function conversion effectively removes the image edge blur and improves the image clarity.

In one embodiment, the acquiring the first image includes receiving a shooting instruction, acquiring the RGB three-channel response value according to the shooting instruction, and performing auto-focus, automatic exposure, and automatic white balance processing on the RGB three-channel response value to obtain an RGB image, Let this RGB image be the first image.

Specifically, the shooting instruction means that the electronic device receives an instruction generated by the user touching or clicking a shooting control on the camera shooting interface. After receiving the shooting instruction, the electronic device projects the subject to the photosensitive surface of the image sensor through the optical imaging system. The image sensor outputs the RGB three-channel response value through the photoelectric effect. The RGB three-channel response value undergoes autofocus, automatic exposure, and automatic After the white balance processing, an RGB image is obtained, and the RGB image is used as the first image. Obtain the first image directly according to the shooting instructions, and then process the first image to ensure the real-time nature of the image.

In one embodiment, the above imaging method further includes: acquiring an image edge area in the second image.

Obtaining a point spread function, using the point spread function to perform an inverse point spread function transformation on the second image to obtain the target image, including: acquiring a point spread function, and using the point spread function to perform an inverse point on the image edge area in the second image The diffusion function is transformed to obtain the target image. Specifically, a point spread function may be used to perform inverse point spread function transformation on the image edge area in the second image, and the amount of processed data is small.

In one embodiment, obtaining a point spread function, and using the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image includes: dividing the second image into a preset number of blocks; acquiring Shooting the point spread function corresponding to each block in the second image; performing inverse point spread function transformation on the corresponding block according to the point spread function corresponding to each block to obtain the target sub-image corresponding to each block, The target sub-image is synthesized to obtain the target image.

Among them, the preset number can be set according to needs, such as 2*2 blocks or 3*3 blocks. Each block can be calculated to obtain the corresponding point spread function. After the electronic device obtains the point spread function corresponding to each block in the second image, the point spread function corresponding to each block performs inverse point spread function transformation on the block to obtain the target sub-image corresponding to each block. The target sub-image is synthesized to obtain the target image.

By dividing the second image into multiple blocks, and using a corresponding point spread function for each block to perform an inverse point spread function transformation, the calculation is more accurate and the image clarity is improved.

In one embodiment, obtaining the point spread function includes: obtaining a reference image for calibration, and a point-diffused sample image taken from the reference image; dividing the sample image and the reference image into corresponding presets respectively A number of blocks; using the blocks in the sample image and the corresponding blocks in the reference image to calculate the point spread function corresponding to the block.

The reference image used for calibration may be a standard resolution test card. The camera of the electronic device shoots the reference image to obtain the diffused sample image. The sample image is the convolution of the reference image and the point spread function. Using equation (3), the sample image is divided by the reference image to obtain the point spread function. The sample image and the reference image are divided into corresponding preset number of blocks, and each block of the sample image corresponds to a block of the reference image.

A block of the sample image and a corresponding block in the reference image are used to calculate the point spread function of the block.

Specifically, a resolution standard test card used for calibration can be selected, and a single-lens reflex camera is used to obtain an image with a small degree of dispersion as a reference image. The reference image can be considered to have almost no PSF diffusion, and then an electronic device with an optical imaging system is used to shoot the test card at the same distance to obtain a sample image with diffuse edges. Compare a block in the reference image with a corresponding block in the sample image, calculate the MTF function at different positions according to the spatial frequency and contrast of the line pair, and perform the two-dimensional inverse Fourier transform on the MTF(u,v) To get PSF(x,y).

In one embodiment, obtaining a point spread function includes: acquiring a black and white hypotenuse image, fitting the black and white hypotenuse image to obtain a distribution function of the edge; calculating an edge spread function according to the distribution function of the edge Derive the diffusion function to obtain a linear diffusion function, and then perform a Fourier transform on the linear diffusion function to obtain a point diffusion function.

Among them, the PSF(u,v) obtained by performing the two-dimensional Fourier transform on the point spread function PSF(x,y) of the point light source imaging can be regarded as the modulation transfer function MTF(u,v) of the optical imaging system Among them, MTF (ModulationTransferFunction) is the modulation transfer function. Therefore, when performing the inverse PSF transformation, the modulation transfer function MTF of the optical imaging system needs to be solved.

Specifically, the electronic device scans the selected black and white oblique-edge images progressively. When the average gray value of the low-reflectivity and high-reflectivity targets on both sides of the edge is equal to the gray value of the pixel or between two adjacent pixels When the gray value is between, the position is the position of the blade edge, the distribution function of the blade edge is fitted, the edge diffusion function is fitted to the distribution function of the blade edge, and the line diffusion function is derived by derivation of the edge diffusion function , And then Fourier transform the linear diffusion function to obtain a modulation transfer function, and use the modulation transfer function as a point diffusion function.

In one embodiment, the above imaging method further includes: acquiring a reference image for calibration, and a point-diffused sample image obtained by shooting the reference image; and calculating a point spread function according to the sample image and the reference image.

The reference image used for calibration may be a standard resolution test card. The camera of the electronic device shoots the reference image to obtain the diffused sample image. The sample image is the convolution of the reference image and the point spread function. Using equation (3), the sample image is divided by the reference image to obtain the point spread function.

Specifically, a resolution standard test card for calibration can be selected, and the camera is used for shooting to obtain an image with a small degree of dispersion as a reference image. The reference image can be considered to have almost no PSF diffusion, and then an electronic device with an optical imaging system is used to shoot the test card at the same distance to obtain a sample image with diffuse edges. Compare the reference image and the sample image, calculate the MTF function at different positions according to the spatial frequency and contrast of the line pair, and perform the two-dimensional inverse Fourier transform on the MTF(u,v) to obtain PSF(x,y).

In one embodiment, the above imaging method further includes: performing noise reduction and sharpening processing on the target image to obtain a corrected image; wherein, in the sharpening processing, the filtering radius is reduced.

Specifically, for noise reduction, a nonlinear de-drying algorithm can be used for noise reduction, such as a bilateral filter. Sharpening refers to compensating the outline of the image, enhancing the edges and gray transitions of the image, and making the image clear. Sharpening processing is generally divided into spatial processing and frequency processing. After the target image is transformed by the inverse point spread function, it can be processed by the sharpening algorithm. By reducing the filter radius, the intensity of the sharpening algorithm is reduced. Reduce the intensity of the sharpening algorithm, thereby effectively alleviating the white fringing phenomenon and improving the image quality.

4 is a flowchart of an imaging method in another embodiment. As shown in FIG. 4, an imaging method includes:

In step 402, the object is projected onto the photosensitive surface of the image sensor through the optical imaging system.

Step 404, the image sensor outputs RGB three-channel response values through the photoelectric effect.

In step 406, the RGB three-channel response value is processed through auto focus, auto exposure, and auto white balance to obtain an RGB image.

Specifically, the RGB image is the first image.

Step 408: Convert the RGB image to a YUV image through the color space.

Specifically, the YUV image is the second image.

Step 410: Perform inverse point spread function conversion on the YUV image to obtain the target image.

Specifically, inverse point diffusion function conversion is performed on the Y channel in the YUV image to obtain the target image.

Step 412: Perform noise reduction and sharpening processing on the target image to obtain a corrected image.

Specifically, the intensity can be reduced during sharpening.

In the above imaging method, by converting the RGB image obtained by the optical imaging system into a YUV image and performing inverse point diffusion function conversion on the YUV image, the circle of confusion on the edge of the image can be effectively removed, the sharpness of the image can be improved, and the sharpening process It can effectively alleviate the white border phenomenon and improve the image quality.

The above imaging method can also be used to reduce the thickness of the optical module, such as reducing the distance between two lenses. The optical module with reduced thickness adopts this imaging method to achieve the same imaging effect as the original module.

The imaging method is described below in conjunction with the most detailed embodiment. An imaging method includes (1) to (8).

(1) Project the subject on the photosensitive surface of the image sensor through the optical imaging system.

(2) The image sensor outputs the RGB three-channel response value through the photoelectric effect.

(3) The RGB three-channel response value is processed by autofocus, autoexposure, and auto white balance to obtain an RGB image, and the RGB image is used as the first image.

(4) Convert the first image to the second image through the color space.

(5) Divide the second image into a predetermined number of blocks.

(6) Obtain the point spread function corresponding to each block in the second image.

(7) Use the point spread function corresponding to each block to perform inverse point spread function transformation on the corresponding block in the second image to obtain the target sub-image corresponding to each block, and synthesize the target sub-image to obtain the target image.

(8) Perform noise reduction and sharpening processing on the target image to obtain a corrected image, in which the filtering radius is reduced in the sharpening processing.

The imaging method in this embodiment converts the acquired first image into a second image containing a luminance channel through a color space, and performs an inverse point diffusion function on the corresponding block in the second image according to the point diffusion function corresponding to each block Conversion to get the target image, inverse point diffusion function conversion for the image, and point diffusion function inverse conversion for the block is more accurate, effectively removing the image edge blur, improving the clarity of the image, by reducing the intensity of the sharpening process, you can Effectively alleviate the white border phenomenon and improve the image quality.

It should be understood that although the steps in the flowcharts of FIG. 2 and FIG. 4 are displayed in order according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless clearly stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least some of the steps in FIGS. 2 and 4 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. These sub-steps or The execution order of the stages is not necessarily sequential, but may be executed in turn or alternately with other steps or sub-steps of the other steps or at least a part of the stages.

5 is a structural block diagram of an imaging device in an embodiment. As shown in FIG. 5, an imaging device includes an image acquisition module 502, an image conversion module 504, and an inverse transform module 506. among them:

The image acquisition module 502 is used to acquire the first image.

The image conversion module 504 is used to convert the first image into a second image containing a luminance channel through a color space.

The inverse transform module 506 is used to obtain a point spread function, and use the point spread function to perform an inverse point spread function transform on the second image to obtain a target image.

The imaging device in this embodiment converts the acquired first image into a second image containing a luminance channel through a color space, then acquires a point spread function, and performs an inverse point spread function conversion on the second image to obtain a target image. The inverse point spread function conversion effectively removes the image edge blur and improves the image clarity.

In one embodiment, as shown in FIG. 6, in addition to the image acquisition module 502, the image conversion module 504, and the inverse transform module 506, the imaging device further includes an area determination module 508, a correction module 510, and a calculation module 512.

The area determination module 508 is used to obtain an image edge area in the second image.

The inverse transform module 506 is also used to obtain a point spread function, and use the point spread function to perform an inverse point spread function transform on the image edge region in the second image to obtain a target image.

The correction module 510 is used to perform noise reduction and sharpening processing on the target image to obtain a corrected image; wherein, in the sharpening processing, the filter radius is reduced.

The calculation module 512 is used to obtain a reference image for calibration, and a point-diffused sample image taken from the reference image, and calculate a point-spread function according to the sample image and the reference image.

In one embodiment, the area determination module 508 is used to divide the second image into a preset number of blocks. The inverse transform module 506 is also used to obtain the point spread function corresponding to each block in the second image; perform the inverse point spread function transformation on the corresponding block according to the point spread function corresponding to each block to obtain each area The target sub-image corresponding to the block, synthesizing the target sub-image to obtain the target image.

The above imaging device may further include a relationship establishing module. The relationship establishing module is used to obtain a reference image used for calibration, and a point-spread sample image obtained by shooting the reference image; divide the sample image and the reference image into corresponding preset number of blocks respectively ; Use the blocks in the sample image and the corresponding blocks in the reference image to calculate the point spread function corresponding to the block, and establish the correspondence between each block and the point spread function.

The division of each module in the above imaging device is for illustration only. In other embodiments, the imaging device may be divided into different modules as needed to complete all or part of the functions of the above imaging device.

7 is a schematic diagram of an internal structure of an electronic device in an embodiment. As shown in FIG. 7, the electronic device includes a processor and a memory connected by a system bus. Among them, the processor is used to provide computing and control capabilities to support the operation of the entire electronic device. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The computer program can be executed by the processor to implement an imaging method provided by the following embodiments. The internal memory provides a cached operating environment for the operating system computer programs in the non-volatile storage medium. The electronic device may be a mobile phone, a tablet computer, a personal digital assistant or a wearable device.

The implementation of each module in the imaging apparatus provided in the embodiments of the present application may be in the form of a computer program. The computer program can be run on a terminal or a server. The program module composed of the computer program may be stored in the memory of the terminal or the server. When the computer program is executed by the processor, the steps of the method described in the embodiments of the present application are implemented.

An embodiment of the present application also provides an electronic device. The above-mentioned electronic device includes an image processing circuit. The image processing circuit may be implemented by hardware and/or software components, and may include various processing units that define an ISP (Image Signal Processing) image signal processing pipeline. 8 is a schematic diagram of an image processing circuit in an embodiment. As shown in FIG. 8, for ease of explanation, only various aspects of the image processing technology related to the embodiments of the present application are shown.

As shown in FIG. 8, the image processing circuit includes a first ISP processor 830, a second ISP processor 840 and a control logic 850. The first camera 810 includes one or more first lenses 812 and a first image sensor 814. The first image sensor 814 may include a color filter array (such as a Bayer filter), the first image sensor 814 may acquire light intensity and wavelength information captured with each imaging pixel of the first image sensor 814, and provide information that can be used by the first ISP A set of image data processed by the processor 830. The second camera 820 includes one or more second lenses 822 and a second image sensor 824. The second image sensor 824 may include a color filter array (such as a Bayer filter), and the second image sensor 824 may acquire the light intensity and wavelength information captured by each imaging pixel of the second image sensor 824 and provide information that can be used by the second ISP A set of image data processed by the processor 840.

The first image collected by the first camera 810 is transmitted to the first ISP processor 830 for processing. After the first ISP processor 830 processes the first image, the statistical data of the first image (such as the brightness of the image, the contrast value of the image , The color of the image, etc.) is sent to the control logic 850, and the control logic 850 can determine the control parameters of the first camera 810 according to the statistical data, so that the first camera 810 can perform autofocus, automatic exposure, etc. according to the control parameters. The first image can be stored in the image memory 860 after being processed by the first ISP processor 830, and the first ISP processor 830 can also read the image stored in the image memory 860 to process the image. In addition, after being processed by the ISP processor 830, the first image can be directly sent to the display 870 for display, and the display 870 can also read the image in the image memory 860 for display.

Among them, the first ISP processor 830 processes image data pixel by pixel in various formats. For example, each image pixel may have a bit depth of 8, 10, 12, or 14 bits, and the first ISP processor 830 may perform one or more image processing operations on the image data and collect statistical information about the image data. Among them, the image processing operation can be performed with the same or different bit depth accuracy.

The image memory 860 may be a part of a memory device, a storage device, or an independent dedicated memory in an electronic device, and may include a DMA (Direct Memory Access) feature.

When receiving the interface from the first image sensor 814, the first ISP processor 830 may perform one or more image processing operations, such as time-domain filtering. The processed image data can be sent to the image memory 860 for additional processing before being displayed. The first ISP processor 830 receives the processed data from the image memory 860 and performs image data processing in the RGB and YCbCr color spaces on the processed data. The image data processed by the first ISP processor 830 may be output to the display 870 for the user to view and/or be further processed by a graphics engine or GPU (Graphics Processing Unit, graphics processor). In addition, the output of the first ISP processor 830 may also be sent to the image memory 860, and the display 870 may read image data from the image memory 860. In one embodiment, the image memory 860 may be configured to implement one or more frame buffers.

The statistical data determined by the first ISP processor 830 may be sent to the control logic 850. For example, the statistical data may include automatic exposure, automatic white balance, automatic focus, flicker detection, black level compensation, first lens 812 shading correction, and other first image sensor 814 statistical information. The control logic 850 may include a processor and/or a microcontroller that executes one or more routines (such as firmware). The one or more routines may determine the control parameters and the first parameters of the first camera 810 based on the received statistical data An ISP processor 830 control parameters. For example, the control parameters of the first camera 810 may include gain, integration time of exposure control, anti-shake parameters, flash control parameters, first lens 812 control parameters (eg, focal length for focusing or zooming), or a combination of these parameters. The ISP control parameters may include gain levels and color correction matrices used for automatic white balance and color adjustment (eg, during RGB processing), and the first lens 812 shading correction parameters.

Similarly, the second image collected by the second camera 820 is transmitted to the second ISP processor 840 for processing. After the second ISP processor 840 processes the first image, the statistical data of the second image (such as the brightness of the image, the image The contrast value of the image, the color of the image, etc.) is sent to the control logic 850, and the control logic 850 can determine the control parameters of the second camera 820 according to the statistical data, so that the second camera 820 can perform autofocus, automatic exposure, etc. according to the control parameters . The second image can be stored in the image memory 860 after being processed by the second ISP processor 840, and the second ISP processor 840 can also read the image stored in the image memory 860 to process it. In addition, after being processed by the ISP processor 840, the second image may be directly sent to the display 870 for display, and the display 870 may also read the image in the image memory 860 for display. The second camera 820 and the second ISP processor 840 may also implement the processing procedures described by the first camera 810 and the first ISP processor 830.

The following are the steps to implement the imaging method using the image processing technology in Figure 8:

The embodiments of the present application also provide a computer-readable storage medium. One or more non-volatile computer-readable storage media containing computer-executable instructions, which when executed by one or more processors, cause the processors to perform the steps of the imaging method.

A computer program product containing instructions that, when run on a computer, causes the computer to perform an imaging method.

Any references to memory, storage, databases, or other media used in embodiments of the present application may include non-volatile and/or volatile memory. Suitable non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR) SDRAM, enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The above-mentioned embodiments only express several implementation manners of the present application, and their descriptions are more specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which all fall within the protection scope of the present application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims (17)

1. An imaging method, including:

   Get the first image;

   Converting the first image into a second image including a brightness channel through a color space; and

   Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image.
2. The method according to claim 1, wherein the method further comprises:

   Acquiring the image edge area in the second image;

   The acquiring a point spread function, and using the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image includes:

   Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the image edge area in the second image to obtain a target image.
3. The method according to claim 1, wherein the acquiring a point spread function and performing an inverse point spread function transformation on the second image using the point spread function to obtain a target image includes:

   Divide the second image into a preset number of blocks;

   Obtaining a point spread function corresponding to each block in the second image; and

   Inverse point diffusion function transformation is performed on the corresponding block according to the point diffusion function corresponding to each block to obtain the target sub-image corresponding to each block, and the target sub-image is synthesized to obtain the target image.
4. The method according to any one of claims 1 to 3, wherein the acquiring a point spread function includes:

   Acquiring a black and white hypotenuse image, fitting the black and white hypotenuse image to obtain a distribution function of the edge; and

   An edge diffusion function is calculated according to the distribution function of the blade edge, the edge diffusion function is derived to obtain a line diffusion function, and then the line diffusion function is Fourier transformed to obtain the point diffusion function.
5. The method according to claim 1 or 2, wherein the method further comprises:

   Performing noise reduction and sharpening processing on the target image to obtain a corrected image;

   In the sharpening process, the filter radius is reduced.
6. The method according to claim 1 or 2, wherein the acquiring a point spread function includes:

   Acquiring a reference image for calibration, and a sample image obtained by shooting the reference image after point diffusion;

   A point spread function is calculated according to the sample image and the reference image.
7. The method according to claim 3, wherein the acquiring a point spread function includes:

   Acquiring a reference image for calibration, and a sample image obtained by shooting the reference image after point diffusion;

   Divide the sample image and the reference image into corresponding preset number of blocks respectively; and

   The block in the sample image and the corresponding block in the reference image are used to calculate the point spread function corresponding to the block.
8. An imaging device, characterized in that it includes:

   The image acquisition module is used to acquire the first image;

   An image conversion module, configured to convert the first image into a second image including a luminance channel through a color space; and

   An inverse transformation module is used to obtain a point spread function, and perform an inverse point spread function transformation on the second image by using the point spread function to obtain a target image.
9. An electronic device includes a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor causes the processor to implement the following steps:

   Get the first image;

   Converting the first image into a second image including a brightness channel through a color space; and

   Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image.
10. The electronic device according to claim 9, wherein the processor is further used to implement:

    Acquiring the image edge area in the second image;

    The acquiring a point spread function, and using the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image includes:

    Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the image edge area in the second image to obtain a target image.
11. The electronic device according to claim 9, wherein the processor is further used to implement:

    Acquiring the image edge area in the second image;

    The acquiring a point spread function, and using the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image includes:

    Obtain a point spread function, and use the point spread function to perform an inverse point spread function transformation on the image edge area in the second image to obtain a target image.
12. The electronic device according to claim 9, wherein the acquiring a point spread function, and using the point spread function to perform an inverse point spread function transformation on the second image to obtain a target image includes:

    Divide the second image into a preset number of blocks;

    Obtaining a point spread function corresponding to each block in the second image; and

    Inverse point diffusion function transformation is performed on the corresponding block according to the point diffusion function corresponding to each block to obtain the target sub-image corresponding to each block, and the target sub-image is synthesized to obtain the target image.
13. The electronic device according to any one of claims 9 to 12, wherein the acquiring point spread function includes:

    Acquiring a black and white hypotenuse image, fitting the black and white hypotenuse image to obtain a distribution function of the edge; and

    An edge diffusion function is calculated according to the distribution function of the blade edge, the edge diffusion function is derived to obtain a line diffusion function, and then the line diffusion function is Fourier transformed to obtain the point diffusion function.
14. The electronic device according to claim 9 or 10, wherein the processor is further used to implement:

    Performing noise reduction and sharpening processing on the target image to obtain a corrected image;

    In the sharpening process, the filter radius is reduced.
15. The electronic device according to claim 9 or 10, wherein the acquiring a point spread function includes:

    Acquiring a reference image for calibration, and a sample image obtained by shooting the reference image after point diffusion; and

    A point spread function is calculated according to the sample image and the reference image.
16. The electronic device according to claim 11, wherein the acquiring a point spread function includes:

    Acquiring a reference image for calibration, and a sample image obtained by shooting the reference image after point diffusion;

    Divide the sample image and the reference image into corresponding preset number of blocks respectively; and

    The block in the sample image and the corresponding block in the reference image are used to calculate the point spread function corresponding to the block.
17. A computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are realized.

Images