CN110363714B - Non-uniformity correction method based on scene interframe registration of self-adaptive learning rate - Google Patents
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Abstract
The invention discloses a scene frame registration-based non-uniformity correction method with adaptive learning rate, which comprises the steps of performing cross-correlation operation on row and column projection vectors of two adjacent frames of images to obtain the displacement of the images in the row and column directions so as to obtain an overlapping area of the two frames of images, and performing iterative updating on correction parameters according to the error of the overlapping area of the two frames of images and the learning rate. The learning rate depends on the spatial features of the image overlapping region, a higher learning rate is used in a flat region, and a lower learning rate is used in a region with more details and edges. Compared with a scene-based non-uniformity correction method using a fixed learning rate, the method has higher convergence rate and can effectively inhibit the generation of a ghost phenomenon.
Description
Technical Field
The invention belongs to the field of infrared image non-uniformity correction, and particularly relates to a non-uniformity correction method based on scene interframe registration and capable of self-adapting to learning rate.
Background
At present, infrared images are widely used in the fields of industry, medicine, military and the like to perform low visibility detection. Under ideal conditions, for infrared light which is uniformly radiated, the gray value of each pixel point on the obtained digital image should be completely the same. However, in practice, the process of manufacturing solid-state electrons is limited, the photosensitive elements (pixels) on the detector often have the problems of uneven impurity concentration, unequal thickness, no absolute average of effective photosensitive area, and the like, the photoelectric conversion efficiency between the pixels is different, and the imaging of uniformly radiated scenery is uneven. In addition, differences between the channels of the image data read-out circuit may cause fixed stripe noise in the image in a column distribution. This requires non-uniformity correction of the image to achieve better visual effect.
The commonly used infrared image non-uniformity correction technology mainly comprises a calibration method and a scene method.
The calibration-based method is a technology that has been adapted at present. However, with the change of the external environment, such as the change of the focal plane temperature and the bias voltage, the response characteristic of the infrared focal plane may drift, so that a new FPN appears on the calibrated and corrected image, and the imaging quality is reduced. In practice, periodic scaling is required to update the correction parameters.
Therefore, in recent years, many scholars have started to research a scene-based nonuniformity correction technique. The correction algorithm based on the scene not only omits a reference radiation source, simplifies the system processing flow, improves the stability of the system, but also can effectively eliminate the influence of parameter characteristic drift and realize the self-adaptive non-uniform correction with high precision and large dynamic range. Algorithms based on scene classes are divided into constant statistical method, neural network algorithm, interframe registration method and the like. However, the neural network non-uniformity correction algorithm is difficult to ensure the correction effect and has higher convergence speed, the engineering application difficulty is higher, and a serious decline phenomenon can occur when the image is static; the constant statistical method has strong dependence on scene random motion and is easily interfered by the scene, and the stability and the convergence speed of the algorithm cannot be considered at the same time; according to the characteristic that the response of each detector element to the same scene in a short time is consistent, the method obtains the response of different pixel elements to the same scene radiation by using an image registration mode. The calculation is simple, engineering application is easy, but convergence speed and stability cannot be considered.
Disclosure of Invention
The invention aims to provide a scene interframe registration-based non-uniformity correction method with a self-adaptive learning rate, which is used for solving the problems that the general scene interframe registration-based non-uniformity correction method is low in convergence speed and easy to generate ghost.
The technical solution for realizing the purpose of the invention is as follows: a non-uniformity correction method based on scene interframe registration of self-adaptive learning rate is realized by the following steps:
step 2, respectively calculating the mean value of all pixel values of each row in the two frames of infrared images, and respectively reducing the mean value of the gray values of the whole image to obtain the row projection value of each row; respectively calculating the mean value of the gray values of all the pixel values of each row, and respectively reducing the mean value of the gray values of the whole image to obtain the row projection value of each row;
step 3, performing cross-correlation operation on the projection values of the rows and the columns in the two frames of infrared images;
and 4, finding out the displacement in the row direction and the column direction when the cross correlation function in the row direction and the column direction is maximum, namely the relative displacement of the two frames of infrared images.
And 5, obtaining the self-adaptive learning rate of each pixel during correction according to the local variance of the pixel.
And 6, obtaining an overlapping area of the two frames of infrared images according to the row and column displacement values of the two frames of infrared images obtained in the step 4, and constructing an error matrix for the overlapping area.
Step 7, updating the correction parameter matrix;
and 8, adding the correction parameters to the original infrared image to obtain an output image.
And 9, executing the operations from the step 2 to the step 8 on all two adjacent frames of the infrared image sequence, namely the 1 st frame and the 2 nd frame, the 2 nd frame and the 3 rd frame, \ 8230, and the k-1 st frame and the k-th frame, \8230, in chronological sequence, and continuously carrying out iterative updating on the correction parameter matrix.
Compared with the prior art, the invention has the remarkable advantages that:
(1) The workload of a calibration method is reduced, and repeated calibration is avoided.
(2) Convergence can be realized only by dozens of frames or even dozens of frames, so that the correction rate is greatly improved;
(3) Because different updating rates are used for different areas, the problems of serious ghost and slow convergence caused by the adoption of a fixed learning rate in a common scene method are solved.
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FIG. 1 is a flow chart of a method for correcting non-uniformity based on scene frame-to-frame registration of adaptive learning rate according to the present invention.
Fig. 2 (a) is a 100 th frame image in an original infrared image sequence having non-uniformity; FIG. 2 (b) is a 100 th frame of image after processing an original image sequence by using a fixed learning rate non-uniformity correction method based on scene frame-to-frame registration; fig. 2 (c) is the 100 th frame image after being processed by the non-uniformity correction method based on scene frame-to-frame registration using the adaptive learning rate of the present invention.
Detailed Description
The following is further detailed in conjunction with the accompanying drawings.
The invention relates to a non-uniformity correction method based on scene interframe registration and capable of realizing self-adaptive learning rate, which can remove non-uniformity in an infrared image sequence through multi-frame iteration. The principle is as follows: the method comprises the steps of firstly calculating cross-correlation information for line projection vectors of front and back two frames of images in an infrared image sequence, then respectively searching horizontal and vertical displacement values which enable the cross-correlation information to be maximum, namely displacement values of the images, obtaining overlapped areas, and updating correction parameters of the images in the overlapped areas according to learning rate to finish non-uniformity correction. An improved method is provided on the basis of a general non-uniformity correction method based on scene frame registration, a self-adaptive learning rate is calculated according to the local variance of each pixel, and ghost can be avoided while the convergence speed is improved.
With reference to fig. 1, a method for correcting heterogeneity based on scene frame-to-frame registration with adaptive learning rate includes the following steps:
step 2, respectively calculating the mean value of all pixel values of each row in the infrared images of the kth frame and the (k + 1) th frame, and respectively subtracting the mean value of the gray values of the corresponding whole infrared images to obtain the row projection value of each row; respectively calculating the mean value of all pixel values of each column, and respectively subtracting the mean value of the gray values of the corresponding whole infrared image to obtain the column projection values of each column, wherein the calculation formula is as follows:
where i denotes the row coordinates of the pixel, j denotes the column coordinates of the pixel,
represents the line projection value of the ith line of the infrared image, is greater than>
Column projection value, X, representing the jth column of the infrared image n (i, j) represents a gray value of a pixel having the coordinate (i, j) in the nth frame; n represents the number of frames, let n = k, or n = k +1;
step 3, performing cross-correlation operation on the projection values of the rows and columns of the infrared images of the k frame and the k +1 frame:
wherein the displacement h ∈ [0,2 × Δ ] in the horizontal direction col ]The displacement v ∈ [0,2 × Δ ] in the vertical direction row ]，Δ col Is the upper limit of the displacement of the k +1 th frame infrared image relative to the k 1 th frame infrared image in the horizontal direction, delta row Setting the upper limit of the displacement of the (k + 1) th frame infrared image relative to the k frame infrared image in the vertical direction; c row (v) As a function of the line-related information of two adjacent frames of infrared images, C col (h) Column cross-correlation information functions of two adjacent frames of infrared images;
step 4, finding out the general formula C row (v) And C col (h) And taking the v, h value corresponding to the maximum value, and calculating the displacement di in the row direction and the displacement dj in the column direction of the infrared image of the (k + 1) th frame relative to the infrared image of the k-th frame:
step 5, the common scene-based inter-frame registration non-uniformity correction method adopts the same fixed learning rate for all pixels, when the learning rate is set to be high, a ghost phenomenon can be generated, and when the learning rate is set to be low, the convergence speed is very low, so that the convergence speed and the stability cannot be well balanced; the invention calculates the self-adaptive learning rate for each pixel according to the local variance of each pixel, and gives consideration to convergence speed and stability, and the method comprises the following specific steps:
5-1) calculating a local mean value m (i, j) of each pixel, where the local mean value refers to a region with a window size of (2l + 1) × (2l + 1) centered on the coordinate (i, j) of the pixel, where l is a preset integer, and X (a, b) represents a gray value of the pixel, and the calculation formula is as follows:
5-2) calculating the local variance σ of each pixel 2 (i, j) the calculation formula is as follows:
5-3) calculating the learning rate of each pixel according to the local variance, wherein the calculation formula is as follows:
wherein alpha is st As the initial learning rate, α (i, j) is the learning rate of the pixel point whose coordinate is (i, j). The learning rate calculated by the formula is higher in an image flat area, the convergence rate can be improved, and is lower in image details and an edge area, so that ghost can be avoided.
Step 6, obtaining the overlapping area of the infrared images of two adjacent frames according to the line and column displacement values di and dj of the infrared images of the k frame and the k +1 frame obtained in the step 4,constructing an error matrix ERR for the overlapping area k+1 (i,j) 2 ：
Wherein, X k (i, j) represents the gray value of the pixel point with the coordinate (i, j) in the k frame infrared image, O k (i, j) represents the value of coordinate (i, j) in the correction parameter matrix calculated with the k-1 and k-th frames, where k-1 is equal to 1 at minimum.
And 7, updating the correction parameter matrix:
O k+1 (i,j)＝O k (i,j)-2×α(i,j)×ERR k+1 (i,j)
step 8, adding the (k + 1) th frame infrared image to a correction parameter matrix to obtain a (k + 1) th frame corrected output image Y k (i,j)：
Y k+1 (i,j)＝X k+1 (i,j)+O k+1 (i,j)
And 9, executing 8230for all two adjacent frames of the infrared image sequence, namely the 1 st frame and the 2 nd frame, the 2 nd frame and the 3 rd frame, and executing 8230for the k-1 st frame and the k-1 rd frame, and continuously carrying out iterative updating on the correction parameter matrix.
Taking the 100 th frame image in the original infrared image sequence with non-uniformity as shown in fig. 2 (a) as an example, processing the original image sequence by using a non-uniformity correction method based on scene frame-to-frame registration with a fixed learning rate to obtain an image as shown in fig. 2 (b), it is obvious that the non-uniformity is still obviously remained, and a ghost is generated. The image obtained by processing the non-uniformity correction method based on scene frame registration with the adaptive learning rate provided by the invention is shown in fig. 2 (c), and compared with fig. 2 (b), the non-uniformity removal effect is better and no ghost phenomenon exists.
Claims (1)
1. A scene frame-to-frame registration-based nonuniformity correction method based on adaptive learning rate is characterized by comprising the following steps:
step 1, obtaining a real-time infrared image sequence through a thermal infrared imager, wherein the resolution of an infrared image is MxN, M is the number of lines, and N is the number of columns; constructing a correction parameter matrix O with the size of M multiplied by N, storing correction parameters of each pixel point, wherein the initial value of the correction parameter matrix O is 0, and selecting two adjacent frames of infrared images for a real-time infrared image sequence every time, such as the kth frame and the (k + 1) th frame, wherein k is more than or equal to 1;
step 2, respectively calculating the mean value of all pixel values of each row in the infrared images of the kth frame and the (k + 1) th frame, and respectively subtracting the mean value of the gray values of the corresponding whole infrared images to obtain the row projection value of each row; respectively calculating the mean value of all pixel values of each column, and respectively subtracting the mean value of the gray values of the corresponding whole infrared image to obtain the column projection value of each column, wherein the calculation formula is as follows:
where i denotes the row coordinate of the pixel, j denotes the column coordinate of the pixel,
a line projection value representing the ith line of the infrared image,
column projection value, X, representing the jth column of the infrared image n (i, j) represents a gray value of a pixel having the coordinate (i, j) in the nth frame; n represents the number of frames, let n = k, or n = k +1;
step 3, performing cross-correlation operation on the projection values of the rows and columns of the infrared images of the k frame and the k +1 frame:
wherein the displacement h ∈ [0,2 × Δ ] in the horizontal direction col ]The displacement v ∈ [0,2 × Δ ] in the vertical direction row ]，Δ col Is the upper limit of the displacement of the k +1 th frame infrared image relative to the k 1 th frame infrared image in the horizontal direction, delta row The upper limit of the displacement of the (k + 1) th frame infrared image relative to the (k) th frame infrared image in the vertical direction is set; c row (v) As a function of the line-related information of two adjacent frames of infrared images, C col (h) Column cross-correlation information functions of two adjacent frames of infrared images;
step 4, finding out the general formula C row (v) And C col (h) And (3) taking the corresponding v, h value when the value is maximum, and calculating the displacement di in the row direction and the displacement dj in the column direction of the (k + 1) th frame infrared image relative to the k frame infrared image:
step 5, obtaining the adaptive learning rate alpha (i, j) when the pixel updates the correction parameter according to the local variance of each pixel, and the specific steps are as follows:
5-1) calculating a local mean value m (i, j) of each pixel, where the local mean value refers to a region with a window size of (2l + 1) × (2l + 1) centered on the coordinate (i, j) of any pixel, where l is a preset integer, and X (a, b) represents a gray value of the pixel, and the calculation formula is as follows:
5-2) calculating the local variance σ of each pixel 2 (i, j), the calculation formula is as follows:
5-3) calculating the learning rate of each pixel according to the local variance, wherein the calculation formula is as follows:
wherein alpha is st α (i, j) is the learning rate of the pixel point of the coordinate (i, j);
step 6, obtaining the overlapping area of the infrared images of two adjacent frames according to the line and column displacement values di and dj of the infrared images of the k frame and the k +1 frame obtained in the step 4, and constructing an error matrix ERR for the overlapping area k+1 (i,j) 2 ：
Wherein, X k (i, j) represents the gray value of a pixel point with coordinates (i, j) in the k frame infrared image, O k (i, j) represents the value of coordinate (i, j) in the correction parameter matrix calculated with the k-1 and k-th frames, where k-1 is at least equal to 1;
and 7, updating the correction parameter matrix:
O k+1 (i,j)＝O k (i,j)-2×α(i,j)×ERR k+1 (i,j)
step 8, adding the infrared image of the (k + 1) th frame and the correction parameter matrix to obtain a corrected output image Y of the (k + 1) th frame k (i,j)：
Y k+1 (i,j)＝X k+1 (i,j)+O k+1 (i,j)
And (3) according to the time sequence, executing the operations of the steps 2 to 8 on all two adjacent frames of the infrared image sequence, namely the 1 st frame and the 2 nd frame, the 2 nd frame and the 3 rd frame, \8230, the k-1 st frame and the k-1 th frame, and the k +1 th frame, \8230, and continuously carrying out iterative updating on the correction parameter matrix.
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