CN115019156A - Blind flash element detection and compensation method based on space-time characteristics - Google Patents
Blind flash element detection and compensation method based on space-time characteristics Download PDFInfo
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Abstract
The invention discloses a blind flash element detection and compensation method based on space-time characteristics, which comprises the following specific processes: step S1: aiming at an infrared image shot by a foundation telescope, detecting blind pixels in the image by utilizing the characteristics that the gray value of the blind pixels is basically unchanged in an image sequence and is different from a normal pixel; step S2: replacing the gray value of the blind pixel by using the eight-neighborhood mean value of the blind pixel; step S3: solving a sequence maximum value image of the image sequence without the blind pixels, and detecting the position of the flash pixels in the sequence image by utilizing the characteristics that the gray value of the flash pixels in the sequence maximum value image has jump compared with the adjacent pixel and the gray value of the flash pixels in the sequence maximum value image is the nine adjacent maximum value; step S4: and for the detected flash elements, counting the gray values of the detected flash elements when the detected flash elements are represented as normal pixels in the K-frame image sequence, and replacing the gray values of the detected flash elements when the detected flash elements are represented as bad elements in the image sequence by the average values of the detected flash elements. The method can detect the blind flash unit and simultaneously avoid inhibiting the target signal.
Description
Technical Field
The invention belongs to the field of ground-based astronomical observation and space detection, and particularly relates to a blind flash element detection and compensation method based on space-time characteristics.
Background
The blind pixels are divided into overheating pixels and dead pixels, wherein the overheating pixels are pixels with pixel noise voltage 10 times larger than average noise voltage ([1] Liu Zheng Long. Infrared image non-uniformity correction and enhancement technology research [ D ]. Harbin engineering university, 2013.), the dead pixels are pixels with output response rate lower than average output response rate 1/10 ([2] Zhao Zhen Man, Song hong fei, ren hong Kai.) an improved scene-based non-uniformity correction method [ J ]. Chun Chang Jing worker university report (Natural science edition), 2020,43(02):53-57.), the pixels are expressed as bright spots or dark spots in images and are easy to be mistakenly detected as targets. Flashes are not always represented as bad in the image sequence ([3] Liu Goui, Sun Sheng, Lin Chang Qing, Lu Yu le.) Infrared line Detector flash noise analysis and suppression method [ J ] Infrared and millimeter wave bulletin, 2018,37(04):421 one 426+ 432.). When it appears as a bad cell, it appears as a bright spot in the image; and when it appears as a normal picture element, it is the same as the other normal picture elements. Therefore, the flash appears as a point that flickers continuously in the image, and is also likely to be erroneously detected as a target.
The reasons for the generation of blind flash are: due to defects of production processes and materials, the infrared detector can randomly generate some blind pixels and flash pixels. The pixels in the infrared detector, which are represented as blind pixels and flash pixels, have a completely or severely failed response to infrared radiation, which deviates significantly from the normal value.
Although a time domain average outlier extraction blind pixel detection algorithm (TMOE) can detect blind pixels, the TMOE algorithm may falsely detect a target as a blind pixel. Therefore, after the blind pixel compensation is performed, the target is suppressed, and the difficulty in detecting the weak and small target is increased.
Conventional flash compensation algorithms include neighborhood median compensation and neighborhood mean compensation, both of which compensate flashes well in the usual case. But both of these methods ignore the useful information that flash elements may carry when they appear as normal picture elements. Therefore, when the image element where the target is located is represented as a flash element, the target is suppressed by both of the two flash element compensation algorithms, and the difficulty of target detection is increased.
Disclosure of Invention
The invention provides a blind flash element detection and compensation method based on space-time characteristics, aiming at overcoming the defects of the existing scheme, and the method is used for avoiding the influence on the detection of a weak target while detecting and compensating the blind flash element. The method overcomes the defect that the TMOE algorithm can falsely detect the target as the blind pixel, fully utilizes the characteristic that the blind flash pixel is different from the weak target, and ensures that the weak target is not falsely detected and inhibited while detecting and compensating the blind flash pixel.
The technical solution of the invention is as follows: a blind flash element detection and compensation method based on space-time characteristics comprises the following implementation steps:
step S1: aiming at an infrared image shot by a foundation telescope, detecting blind pixels in the image by utilizing the characteristics that the gray value of the blind pixels is basically unchanged in an image sequence and is different from a normal pixel;
step S2: replacing the gray value of the blind pixel by using the eight-neighborhood mean value of the blind pixel;
step S3: solving a sequence maximum value image of the image sequence without the blind pixels, and detecting the position of the flash pixels in the sequence image by utilizing the characteristics that the gray value of the flash pixels in the sequence maximum value image has jump compared with the adjacent pixel and the gray value of the flash pixels in the sequence maximum value image is the nine adjacent maximum value;
step S4: and for the detected flash elements, counting the gray values of the detected flash elements when the detected flash elements are represented as normal pixels in the K-frame image sequence, and replacing the gray values of the detected flash elements when the detected flash elements are represented as bad elements in the image sequence by the average values of the detected flash elements.
The principle of the invention is as follows: the gray value of the blind flash element in the sequence image is basically unchanged, the gray value of the flash element jumps, and the blind flash element is obviously different from the neighborhood pixel in the airspace. The blind flash pixel detection and compensation method based on the space-time characteristic fully utilizes the characteristics that the gray value of the blind pixel is basically unchanged in an image sequence and is different from a normal pixel, and the gray value of the flash pixel has jumping compared with a neighborhood pixel, greatly reduces the false alarm rate of weak and small target detection, and improves the detection rate of the weak and small target detection.
Compared with the prior art, the invention has the advantages that:
(1) the blind flash element detection and compensation algorithm based on the space-time characteristics overcomes the defect that the traditional blind element detection algorithm can falsely detect the target as the blind element, and improves the detection probability of target detection.
(2) When the pixel where the target is located is expressed as the flash element, the blind flash element detection and compensation algorithm based on the space-time characteristic can keep the information when the pixel is expressed as the normal pixel, and the defect that the target can be inhibited by the traditional flash element compensation algorithm is overcome.
(3) The infrared target image processing method adopts a blind flash element detection and compensation algorithm based on the space-time characteristics, can accurately and efficiently detect and compensate the blind flash elements in the infrared image, and reduces the false alarm rate of weak and small target detection while ensuring the target detection effect.
Drawings
FIG. 1 is a flow chart of a blind flash detection and compensation method based on spatiotemporal characteristics according to the present invention;
FIG. 2a is a schematic diagram of an original image including a blind flash cell according to the present invention;
FIG. 2b is a three-dimensional view of an original image according to the present invention;
FIG. 3 is a schematic diagram of a blind pixel detection result according to the present invention;
FIG. 4a is a schematic diagram illustrating a blind pixel compensation result according to the present invention;
FIG. 4b is a three-dimensional diagram of the blind pixel compensation result of the present invention;
FIG. 5a is a sequence maximum image diagram of the present invention;
FIG. 5b is a three-dimensional view of a sequence maxima image of the present invention;
FIG. 6 is a diagram illustrating a flash detection result according to the present invention;
FIG. 7a is a diagram illustrating flash compensation results according to the present invention;
FIG. 7b is a three-dimensional diagram of the flash compensation result of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
As shown in FIG. 1, the blind flash element detection and compensation method based on the space-time characteristics is suitable for reducing the influence of the blind flash element of the infrared detector on the detection of the weak and small targets when the ground-based telescope shoots the targets. Specifically, the blind flash element detection and compensation method based on the space-time characteristics comprises the following steps:
step S1: aiming at an infrared image shot by a foundation telescope, detecting blind pixels in the image by utilizing the characteristics that the gray value of the blind pixels is basically unchanged in an image sequence and is different from a normal pixel;
step S2: replacing the gray value of the blind pixel by using the eight-neighborhood mean value of the blind pixel;
step S3: and solving a sequence maximum value image of the image sequence without the blind pixels, and detecting the position of the flash pixels in the sequence image by utilizing the characteristics that the gray value of the flash pixels in the sequence maximum value image has jump compared with the adjacent pixel and the gray value of the flash pixels in the sequence maximum value image is the nine adjacent maximum values.
Step S4: and for the detected flash elements, counting the gray values of the detected flash elements when the detected flash elements are represented as normal pixels in the K-frame image sequence, and replacing the gray values of the detected flash elements when the detected flash elements are represented as bad elements in the image sequence by the average values of the detected flash elements.
The process of the invention is illustrated in FIG. 1. Specifically, the blind pixel detection and compensation algorithm of the invention comprises the following steps:
1. as shown in FIG. 2a, the original infrared image is obtained by taking K continuous infrared images and performing time domain averaging to obtain an average image
In the formula, I (I, j, K), K is 1,2, …, K, and represents the gray scale value of the pixel located in the ith row and jth column in the kth frame image.
2. For average image
Is subjected to mirror image expansion, and the image after the expansion is carried out
To Chinese
Taking a window with the size of s multiplied by s as the center, calculating the gray level median of the pixels in the window, and recording the gray level median as
i 1 ＝i+s，j 1 ＝j+s。
3. Computing pixels
The absolute value of the difference between the time-domain mean value of gray scale and the median value of gray-domain space, i.e., the outlier D (i, j):
4. and (4) setting a reasonable threshold Thr, if D (I, j) > Thr, performing the step 5, otherwise, judging the pixel I (I, j) to be a normal pixel, and returning to the step 2. Setting Thr as:
Thr＝μ D +tσ D (3)
in the formula, mu D Representing the mean value, σ, of all pixel outliers of a single frame image D Denotes the standard deviation of the outliers, and t is constant.
In the formula, M and N respectively represent the total number of rows and the total number of columns of a single frame image.
5. And when a certain pixel meets the threshold judgment condition of the step 4, counting the total frame number of the pixel with the gray value of the pixel being the maximum value of the nine neighborhoods in the K frame image sequence, and recording as z. The blind pixel judgment conditions are set as follows:
6. the blind pixel detection result is shown in fig. 3, the pixels marked by the black circular frame and the white rectangular frame in fig. 3 are detected blind pixels, and only the pixels marked by the black circular frame satisfy the judgment condition in the step 4 but do not satisfy the judgment condition in the step 5, and may be a target, a flash pixel and a normal pixel. For the pixel detected as the blind pixel, calculating the average value of the eight neighborhood gray levels in each frame of image, and replacing the gray level of the blind pixel in the frame of image with the average value to obtain an image sequence I completing the detection and compensation of the blind pixel 1 . The blind compensation result is shown in fig. 4 a.
The flash element detection and compensation algorithm comprises the following steps:
1. in order to avoid failure of flash detection caused by too large fluctuation of the background of the sky light, a K frame image sequence I is calculated first 1 Background mean value m of each frame of image B (k)：
To m B (k) Sorting 20 frames of images in the middle according to the sequence from small to large, and recording the images as I 2 (k 1 ) The background mean value of the 20 frames of images is recorded as m B1 (k 1 )，k 1 ＝1,2,…,20。
2. To obtain I 2 In 20 frames for each picture elementThe maximum value of gray scale in the image is obtained to obtain a sequence maximum value image I max1 As shown in fig. 5 a.
3. Will I max1 The edge mirror image of (1) extends 1 row and 1 column to obtain an image I max2 。
4. Traverse image I from top to bottom and from left to right max2 . To I max2 Pixel I in (1) max2 (i 2 ,j 2 )，i 2 ＝i+1，j 2 Calculating the maximum value t of the nine neighborhood gray scale as j +1 1 . If the pixel I max2 (i 2 ,j 2 ) Is equal to t 1 And (5) carrying out the step; otherwise, the next pixel is judged.
5. Calculating a pixel I max2 (i 2 ,j 2 ) The difference of the gray values of all pixels in the eight neighborhoods is summed and averaged to obtain a pixel I max2 (i 2 ,j 2 ) And the average variation v (i, j) of the gray value of each pixel in the eight neighborhoods:
in the formula I s (j 1 ) Representing picture elements I max2 (i 2 ,j 2 ) Gray value j of the eight neighborhood pixels 1 ＝1,2,…,8。
6. Calculating a pixel I max2 (i 2 ,j 2 ) Eight neighborhood gray level mean v 1 ：
7. Calculating m B1 (k) Mean value m of 1 And standard deviation σ 1 ：
8. Setting flash element judgment conditions:
when the temperature is higher than the set temperature
When sigma is 1 When the background changes more severely when the background is more than 3, setting the parameter r as 1; when sigma is 1 If the background light is less than 0.8, the background light does not change greatly, and the parameter r is set to be 5; when the value of sigma is more than or equal to 0.8 1 When less than or equal to 3, the background change of the daylight is gentle, and the parameter r is set to be sigma 1 。
The flash detection result is shown in fig. 6, and the pixels marked by the white circular frames in the figure are flash.
1. For pixel I detected as flash element by the above steps 1 (x, y), sequentially judging the K frame image sequence as follows:
when in use
Counting pixels I in K frame image 1 (I, j) the gray values when represented as normal pixels, and replacing pixel I with their mean value 1 (i, j) is expressed as a gray value in the case of a bad cell. The flash compensation results are shown in fig. 7 a.
In order to compare the superiority of the flash compensation algorithm of the invention compared with the traditional algorithm, when the pixel of the target shows flash, the algorithm of the invention, the neighborhood mean compensation algorithm and the neighborhood median compensation algorithm are respectively used for blind flash detection and compensation on the same image sequence, then the image sequence is subjected to multi-frame accumulation, and the mean signal-to-noise ratio (MSNR) of the accumulated target is shown in the following table:
TABLE 1 comparison of target MSNR obtained from multi-frame accumulation after processing by the inventive and conventional algorithms
As can be seen from Table 1, when the pixel where the target is located represents a flash element, the algorithm of the present invention can obtain the highest signal-to-noise ratio of the target mean value under the same accumulated frame number, which indicates that the algorithm of the present invention has the least suppression on the target signal when compensating the flash element, and can reduce the occurrence of target missing detection and improve the detection rate of target detection.
Claims (5)
1. A blind flash element detection and compensation method based on space-time characteristics is characterized in that the method comprises the following implementation steps:
step S1: aiming at an infrared image shot by a foundation telescope, detecting blind pixels in the image by utilizing the characteristics that the gray value of the blind pixels is basically unchanged in an image sequence and is different from a normal pixel;
step S2: replacing the gray value of the blind pixel by using the eight-neighborhood mean value of the blind pixel;
step S3: solving a sequence maximum value image of the image sequence without the blind pixels, and detecting the position of the flash pixels in the sequence image by utilizing the characteristics that the gray value of the flash pixels in the sequence maximum value image has jump compared with the adjacent pixel and the gray value of the flash pixels in the sequence maximum value image is the nine adjacent maximum value;
step S4: and for the detected flash elements, counting the gray values of the detected flash elements when the detected flash elements are represented as normal pixels in the K-frame image sequence, and replacing the gray values of the detected flash elements when the detected flash elements are represented as bad elements in the image sequence by the average values of the detected flash elements.
2. The spatiotemporal characteristic-based blind flash detection and compensation method according to claim 1, wherein the blind flash detection step in step S1 is as follows:
step 21: taking K continuous infrared images, and performing time domain averaging to obtain an average image
In the formula, I (I, j, K), K is 1,2, …, K, and represents the gray scale value of the pixel located in the ith row and jth column in the kth frame image;
step 22: for average image
Is subjected to mirror image expansion, and the image after the expansion is carried out
To Chinese
Taking a window with the size of s multiplied by s as the center, calculating the gray level median of the pixels in the window, and recording the gray level median as
i 1 ＝i+s，j 1 ＝j+s；
Step 23: computing pixels
The absolute value of the difference between the time-domain mean value of gray scale and the median value of gray-domain space, i.e., the outlier D (i, j):
step 24: setting a reasonable threshold Thr, if D (I, j) > Thr, performing the step 25, otherwise, judging the pixel I (I, j) to be a normal pixel, returning to the step 22, and setting Thr as:
Thr＝μ D +tσ D (3)
in the formula, mu D Representing the mean value, σ, of all pixel outliers of a single frame image D Representing the standard deviation of the outlier, t is a constant;
in the formula, M and N respectively represent the total row number and the total column number of a single-frame image;
step 25: when a certain pixel meets the threshold judgment condition in step 24, counting the total number of frames in the K-frame image sequence, where the gray value of the pixel is the maximum value of the nine neighborhoods, and is denoted as z, and setting blind pixel judgment conditions as follows:
3. the method for detecting and compensating blind flash based on spatio-temporal characteristics according to claim 1, wherein the blind flash compensation step in step S2 is as follows:
step 31: and calculating the average value of the gray levels of eight neighborhoods of the blind pixels in each frame of image, and replacing the gray values of the blind pixels in the frame of image with the average value.
4. The spatio-temporal characteristic-based blind flash detection and compensation method according to claim 1, wherein the flash detection step in step S3 is as follows:
step 41: image sequence I for completing blind pixel detection and compensation through calculation 1 Background mean value m of each frame of image B (k)：
To m B (k) Sorting 20 frames of images in the middle according to the sequence from small to large, and recording the images as I 2 (k 1 ) The background mean value of the 20 frames of images is recorded as m B1 (k 1 )，k 1 ＝1,2,…,20；
Step 42: to obtain I 2 Each image inObtaining a sequence maximum value image I by taking the maximum value of the gray scale in the 20 frame image as an element max1 ；
Step 43: will I max1 The edge mirror image of (1) extends 1 row and 1 column to obtain an image I max2 ；
Step 44: traversing the image I from top to bottom and from left to right max2 To 1, pair max2 Pixel I in (1) max2 (i 2 ,j 2 )，i 2 ＝i+1，j 2 Get the maximum value t of the nine neighborhood gray scale as j +1 1 If the pixel element I max2 (i 2 ,j 2 ) Is equal to t 1 Step 45 is performed; otherwise, judging the next pixel;
step 45: calculating a pixel I max2 (i 2 ,j 2 ) The difference of the gray values of all pixels in the eight neighborhoods is summed and averaged to obtain a pixel I max2 (i 2 ,j 2 ) And the average variation v (i, j) of the gray value of each pixel in the eight neighborhoods:
in the formula I s (j 1 ) Representing picture elements I max2 (i 2 ,j 2 ) Gray value j of the eight neighborhood pixels 1 ＝1,2,…,8；
Step 46: calculating a pixel I max2 (i 2 ,j 2 ) Eight neighborhood gray level mean v 1 ：
Step 47: calculating m B1 (k) Mean value m of 1 And standard deviation σ 1 ：
And step 48: setting flash element judgment conditions:
when in use
When sigma is 1 When the background changes more severely when the background is more than 3, setting the parameter r as 1; when sigma is 1 If the background light is less than 0.8, the background light does not change greatly, and the parameter r is set to be 5; when the value of sigma is more than or equal to 0.8 1 When less than or equal to 3, the background change of the daylight is gentle, and the parameter r is set to be sigma 1 ；
Step 49: for pixel I detected as flash element by the above steps 1 (x, y), sequentially judging the K frame image sequence as follows:
when in use
。
5. The spatio-temporal characteristic-based blind flash detection and compensation method according to claim 1, wherein the flash compensation step in step S4 is as follows:
step 51: counting pixels I in K frame image 1 (x, y) are represented as gray values in normal pixels and the pixel I is replaced by their mean value 1 (x, y) is expressed as a gray value in the case of a bad cell.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117197682A (en) * | 2023-09-01 | 2023-12-08 | 山东产研卫星信息技术产业研究院有限公司 | Method for blind pixel detection and removal by long-wave infrared remote sensing image |
| CN117197682B (en) * | 2023-09-01 | 2024-06-25 | 山东产研卫星信息技术产业研究院有限公司 | Method for blind pixel detection and removal by long-wave infrared remote sensing image |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117197682A (en) * | 2023-09-01 | 2023-12-08 | 山东产研卫星信息技术产业研究院有限公司 | Method for blind pixel detection and removal by long-wave infrared remote sensing image |
| CN117197682B (en) * | 2023-09-01 | 2024-06-25 | 山东产研卫星信息技术产业研究院有限公司 | Method for blind pixel detection and removal by long-wave infrared remote sensing image |
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