CN111539967B - Method and system for identifying and processing interference fringe region in terahertz imaging of focal plane - Google Patents

Abstract

The invention discloses a method and system for identifying and processing interference fringe areas in focal plane terahertz imaging, comprising: performing global threshold segmentation on an image obtained in focal plane terahertz imaging, acquiring the global threshold of the image, and obtaining a first image; judging Whether the brightness of the first image is uneven: if yes, perform local threshold segmentation on the first image to obtain the second image, perform stripe recognition on the second image, obtain the dark stripe area of the second image, and combine the dark stripe area of the first image and Merge the dark stripe areas of the second image and perform stripe elimination to obtain a third image; otherwise, perform stripe recognition on the first image to obtain the dark stripe areas of the first image and perform stripe elimination processing to obtain a fourth image. performing image enhancement processing on the third image or the fourth image. The invention can eliminate the interference of large-area interference fringes on the target object in the image, and can also greatly improve the image quality.

Description

A method and system for identifying and processing interference fringe areas in focal plane terahertz imaging

Technical Field

The present invention relates to the field of focal plane terahertz imaging, and in particular to a method and system for identifying and processing interference fringe regions in focal plane terahertz imaging.

Background Art

Terahertz radiation refers to electromagnetic waves with a frequency between 0.1-10THz, which is between microwaves and infrared and belongs to the category of far-infrared electromagnetic radiation. With the progress of terahertz wave sources and detectors, continuous terahertz imaging systems are constantly seeking breakthroughs in terms of small size, fast speed, and easy operation. Terahertz focal plane array detectors have a wide range of application scenarios due to their low cost, easy operation, fast acquisition speed, and high image quality signal-to-noise ratio.

However, due to the long wavelength of terahertz, a very obvious interference phenomenon occurs on the optical path from the terahertz wave source to the detection end, resulting in large-area interference fringes in the background of the final collected image, which greatly affects the imaging of the target. In addition, due to the low energy of terahertz itself, the overall contrast of the image is low, and image enhancement is required. In the prior art, researchers have done a lot of research on this, but most of them are still direct high-frequency filtering or adding band-stop filters, which can only solve the stripe noise that is strictly distributed on the entire image. Once the interference fringes appear only locally or the distribution is slightly messy, it cannot be processed, and conventional image enhancement methods will also enhance the interference of the fringes. Therefore, an effective method is needed to identify and process the fringes in terahertz images in a targeted manner.

Summary of the invention

The main technical problem solved by the present invention is to provide a method for identifying fringes in a terahertz image, which can perform targeted identification and processing on the fringes in the terahertz image.

In order to solve the above technical problems, a technical solution adopted by the present invention is to provide a method for identifying and processing interference fringe areas in focal plane terahertz imaging.

The method comprises the following steps:

Performing global threshold segmentation on the image obtained in the focal plane terahertz imaging, obtaining a global threshold of the image, and obtaining a first image;

Determine whether the first image has uneven brightness: if yes, perform local threshold segmentation on the first image to obtain a second image, perform stripe recognition on the second image to obtain a dark stripe area of the second image; otherwise, perform stripe recognition on the first image to obtain a dark stripe area of the first image;

If there is uneven brightness, the dark stripe area of the first image and the dark stripe area of the second image are merged and stripe elimination processing is performed to obtain a third image; otherwise, the dark stripe area of the first image is subjected to stripe elimination processing to obtain a fourth image.

Perform image enhancement processing on the third image or the fourth image.

Preferably, before performing global threshold segmentation, the image obtained in the focal plane terahertz imaging is linearly stretched. The global threshold segmentation includes using a single threshold segmentation based on the maximum inter-class variance method, namely, OTSU.

Preferably, determining whether the first image has uneven brightness includes:

Calculate the difference of four upper, lower, left and right boundary areas of the first image respectively, where the four upper, lower, left and right boundary areas refer to four rectangular areas close to the edge of the first image;

Find the maximum difference among the four boundary regions;

If it is determined that the maximum absolute value of the difference is greater than a threshold, the brightness of the first image is uneven.

Preferably, performing multiple local threshold segmentation according to the uneven brightness to obtain a fifth image; performing image enhancement processing on the third image or the fourth image includes using an enhanced auxiliary binary image, and the enhanced auxiliary binary image includes:

When the first image does not have uneven brightness, the enhanced auxiliary binary image uses the first image;

When the first image has uneven brightness, if the local threshold segmentation is performed only once, the enhanced auxiliary binary image uses the second image; otherwise, the enhanced auxiliary binary image uses the fifth image.

Preferably, when the maximum difference is a positive value, the average coordinate position of the bright pixels in the first image is calculated; when the maximum difference is a negative value, the average coordinate position of the dark pixels in the first image is calculated.

Preferably, the stripe recognition includes: obtaining a binary image with better features in the first image, scanning the binary image row by row or column by column in the direction of the stripes, and saving the continuous length of bright pixels and dark pixels into a length segment; determining the part of the dark stripes in the length segment, restoring it to the pixel position of the original image and marking it in a marking matrix of the same size as the original image.

Preferably, the stripe recognition further comprises finding similar points within a certain distance around the marking points in the marking matrix.

Preferably, performing image enhancement processing on the third image or the fourth image includes performing grayscale enhancement processing on pixels in a dark stripe area of background pixels of the third image or the fourth image.

Preferably, after the grayscale enhancement process is performed on the third image or the fourth image, a noise reduction process is performed using a mean filtering method.

A system for identifying and processing interference fringe regions in focal plane terahertz imaging comprises:

A global threshold segmentation module: used for performing global threshold segmentation on the image obtained in the focal plane terahertz imaging, obtaining a global threshold of the image, and obtaining a first image;

Local threshold segmentation module: determine whether the first image has uneven brightness: if yes, perform local threshold segmentation on the first image to obtain a second image, perform stripe recognition on the second image to obtain a dark stripe area of the second image; otherwise, perform stripe recognition on the first image to obtain a dark stripe area of the first image;

Stripe merging and elimination module: if there is uneven brightness, the dark stripe area of the first image and the dark stripe area of the second image are merged and stripe elimination is performed to obtain a third image; otherwise, the dark stripe area of the first image is subjected to stripe elimination to obtain a fourth image.

A system for identifying and processing interference fringe regions in focal plane terahertz imaging also includes:

Image enhancement module: performs image enhancement processing on the third image or the fourth image.

The beneficial effects of the present invention are:

(1) The terahertz image is processed by combining global threshold segmentation with local threshold segmentation, which can automatically identify areas with uneven brightness and perform corresponding processing; at the same time, the stripe area is accurately identified and the dark stripes are processed to eliminate the interference of large-area stripes in the image on the target object; (2) The binary image obtained by local threshold segmentation is enhanced to make the target object more obvious; (3) Compared with the traditional high-frequency filtering plus some band-stop filter processing method, this method has a simpler calculation process, better effect, wider applicability, and great practical value; (4) A system for identifying and processing interference stripe areas in focal plane terahertz imaging can achieve real-time imaging and can avoid the influence of interference stripes to the greatest extent, thereby improving image quality.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a general flow chart of the present invention;

Figure 2 is the original image collected;

Figure 3 is the image after linear stretching;

FIG4 is a binary image obtained by global threshold image segmentation;

FIG5 is a binary image obtained by segmenting a local threshold image when the brightness is uneven;

FIG6 is a binary image obtained by the second local threshold segmentation for image enhancement assistance;

Figures 7 and 8 are the stripe recognition results of Figures 4 and 5, respectively, where the black part is the dark stripe area;

FIG9 is a result of merging the dark stripe regions of FIG7 and FIG8 ;

FIG10 is a diagram showing the effect of eliminating dark streaks;

FIG11 is a diagram showing the effect of image enhancement.

DETAILED DESCRIPTION

In order to better understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

Example 1

In an exemplary embodiment, a method for identifying and processing interference fringe regions in focal plane terahertz imaging as shown in FIG1 includes the following steps:

Performing global threshold segmentation on the image obtained in the focal plane terahertz imaging, obtaining a global threshold of the image, and obtaining a first image;

Determine whether the first image has uneven brightness: if yes, perform local threshold segmentation on the first image to obtain a second image, perform stripe recognition on the second image to obtain a dark stripe area of the second image; otherwise, perform stripe recognition on the first image to obtain a dark stripe area of the first image;

If there is uneven brightness, the dark stripe area of the first image and the dark stripe area of the second image are merged and stripe elimination processing is performed to obtain a third image; otherwise, the dark stripe area of the first image is subjected to stripe elimination processing to obtain a fourth image.

Perform image enhancement processing on the third image or the fourth image.

In the prior art, the difficulty in segmenting focal plane terahertz images is poor contrast. The overall image is blurred and it is not easy to see the shape clearly. There is also the influence of stripes. At the same time, the illumination distribution is uneven. The present invention combines global threshold segmentation with local threshold segmentation, specifically combines the characteristics of focal plane terahertz images, and directly identifies the corresponding positions of interference fringes in the image based on the spatial distribution law of interference fringes, so as to eliminate them in a targeted manner.

Preferably, before performing global threshold segmentation, the image obtained in the focal plane terahertz imaging is linearly stretched. The global threshold segmentation includes using a single threshold segmentation based on the maximum inter-class variance method, namely, OTSU.

Preferably, determining whether the first image has uneven brightness includes:

Calculate the difference of four upper, lower, left and right boundary areas of the first image respectively, where the four upper, lower, left and right boundary areas refer to four rectangular areas close to the edge of the first image;

Find the maximum difference among the four boundary regions;

If it is determined that the maximum absolute value of the difference is greater than a threshold, the brightness of the first image is uneven.

Furthermore, the difference between the four boundary areas of the image is calculated respectively when the global threshold is plus or minus 20. In an image of size M*N, the four boundary areas are 0<x<M/10, 0<y<N/10, 0.9×M<x<M, 0.9×N<y<N, and the difference P is calculated as follows:

Among them, _l1 is the number of bright pixels after regional binarization when the global threshold is plus 20, d2 is the number of dark pixels after regional binarization when the global threshold is minus 20, and l and d are the numbers of bright pixels and dark pixels when the threshold is not changed.

Furthermore, determining whether the first image has uneven brightness further includes finding the maximum difference among the four boundary areas, and when the absolute value of the maximum difference is greater than 0.5, the first image has uneven brightness.

Furthermore, multiple local threshold segmentations are performed according to the uneven brightness. When the local threshold segmentation is performed in S2, the global threshold in S1 is adjusted.

Furthermore, when the maximum difference is a positive value, the average coordinate position p ₀ of the bright pixels in the first image is calculated; when the maximum difference is a negative value, the average coordinate position of the dark pixels in the first image is calculated. The dark pixel coordinate position is recorded as D(a,b), and the threshold in the vertical direction of the original value range U from D to p ₀ is adjusted.

When this direction is horizontal, the adjustment formula is as follows:

min(l，a)＜i≤max(l，a)

min(l, a)＜i≤max(l, a)

When this direction is vertical, the adjustment formula is as follows:

min(l，b)＜j≤max(l，b)

min(l, b)＜j≤max(l, b)

Wherein, T is the adjusted threshold, _T0 is the global threshold, Q is the adjustment amplitude, L=0 when horizontally to the left or vertically upward, L=M when horizontally to the right, and L=N when vertically downward, and M and N are the length and width of the image.

Further, performing multiple local threshold segmentation according to the uneven brightness to obtain a fifth image; performing image enhancement processing on the third image or the fourth image includes using an enhanced auxiliary binary image, and the enhanced auxiliary binary image includes:

When the first image does not have uneven brightness, the enhanced auxiliary binary image uses the first image;

When the first image has uneven brightness, if the local threshold segmentation is performed only once, the enhanced auxiliary binary image uses the second image; otherwise, the enhanced auxiliary binary image uses the fifth image.

Furthermore, if the absolute value of the difference degree p ₁ of the reverse region of U is greater than 0.3, a local threshold adjustment process from D to p ₁ is required. The obtained binary image is used as an enhanced auxiliary binary image in the image enhancement process.

Furthermore, the stripe recognition includes: obtaining a binary image with better features in the first image, scanning the binary image row by row or column by column in the direction of the stripes, and saving the continuous length of bright pixels and dark pixels into a length segment; determining the part of the dark stripes in the length segment, restoring it to the pixel position of the original image and marking it in a marking matrix of the same size as the original image.

Furthermore, the stripe recognition also includes finding similar points within a certain distance around the marked points in the marking matrix.

Furthermore, the image enhancement processing of the third image includes performing grayscale enhancement processing on the background pixels and dark stripe area pixels of the third image.

Furthermore, after the grayscale enhancement process is performed on the third image, a noise reduction process is performed using a mean filtering method.

Example 2

A system for identifying and processing interference fringe regions in focal plane terahertz imaging comprises:

A global threshold segmentation module: used for performing global threshold segmentation on the image obtained in the focal plane terahertz imaging, obtaining a global threshold of the image, and obtaining a first image;

Local threshold segmentation module: determine whether the first image has uneven brightness: if yes, perform local threshold segmentation on the first image to obtain a second image, perform stripe recognition on the second image to obtain a dark stripe area of the second image; otherwise, perform stripe recognition on the first image to obtain a dark stripe area of the first image;

Stripe merging and elimination module: if there is uneven brightness, the dark stripe area of the first image and the dark stripe area of the second image are merged and stripe elimination is performed to obtain a third image; otherwise, the dark stripe area of the first image is subjected to stripe elimination to obtain a fourth image.

Image enhancement module: performs image enhancement processing on the third image or the fourth image.

Specifically, in an exemplary embodiment, after imaging the blade using a smaller focal plane terahertz detector scanning imaging system, the original image shown in FIG2 is obtained. It can be seen that the right half of the image has regular light and dark stripes, which is caused by interference in the optical path from the terahertz wave source end to the detection end. At the same time, the image contrast is low, and it is difficult to identify the blade in the image. The present invention proposes a method for identifying and processing interference stripe areas in focal plane terahertz imaging. The flow chart is shown in FIG1, and includes the following processes:

Linear pull-up process: Using the histogram linear pull-up to improve the contrast of Figure 2 can improve the accuracy of threshold segmentation to a certain extent. The result is shown in Figure 3.

Global threshold segmentation process: Single threshold image segmentation based on the maximum between-class variance method (OTSU) is used for Figure 3 to obtain a binary image that can better reflect the characteristics of stripes and objects, as shown in Figure 4. Ideally, the black part should include the actual imaging target and dark stripes, and the white part is the background. At the same time, the global threshold needs to be retained for subsequent local threshold segmentation and stripe elimination.

Local threshold segmentation process: Depending on whether uneven brightness occurs, it is determined whether to use a new local threshold segmentation method to obtain an improved binary image, as shown in Figure 5. The new binary image can separate the target from the background in the lower half of the image, and better show the difference between the stripes and the blade and the background. The result of the secondary use of local threshold segmentation required for image enhancement is shown in Figure 6, and the actual shape of the entire blade can be clearly seen.

The specific method for judging uneven brightness is to calculate the difference between the four boundary areas of the whole image when the global threshold is added or subtracted by 20. In an image of size M*N, the four boundary areas are 0<x<M/10, 0<y<N/10, 0.9×M<x<M, 0.9×N<y<N, and the calculation method of the difference P is:

Among them, _l1 is the number of bright pixels after regional binarization when the global threshold is plus 20, d2 is the number of dark pixels after regional binarization when the global threshold is minus 20, l and d are the number of bright pixels and dark pixels when the threshold is not changed. Find the largest difference among the four regions. When the absolute value of the largest difference is greater than 0.5, it can be judged that there is uneven brightness in the image.

The local threshold segmentation method is specifically as follows: in Figure 4 obtained by global threshold segmentation, when the maximum difference is a positive value, the average coordinate position p ₀ of the bright pixels is calculated; otherwise, the average coordinate position of the dark pixels is calculated and recorded as D(a,b). The threshold in the vertical direction of the original value area U from D to p ₀ is adjusted.

When this direction is horizontal, the adjustment formula is as follows:

min(l，a)＜i≤max(l，a)

min(l, a)＜i≤max(l, a)

When this direction is vertical, the adjustment formula is as follows:

min(l，b)＜j≤max(l，b)

min(l, b)＜j≤max(l, b)

Wherein, T is the adjusted threshold, _T0 is the global threshold, Q is the adjustment amplitude, L=0 when horizontally to the left or vertically upward, L=M when horizontally to the right, and L=N when vertically downward, and M and N are the length and width of the image.

In particular, in order to obtain the binary image required in the image enhancement step, if the absolute value of the difference p ₁ in the reverse region of U is greater than 0.3, a local threshold adjustment process from D to p ₁ is required.

Stripe recognition process: In the binary image, according to the interference law of light, the interference area is determined from the width, direction and level of the light and dark stripes, and the dark stripe part is identified. As can be seen from Figure 3, the stripes on the right side of the blade clearly conform to the law of interference stripes. Figures 7 and 8 are the results of Figures 5 and 6 after stripe recognition, and the black part is the interference dark stripe part identified in the original image. The specific recognition method is:

In the first step, a binary image with better features is obtained in the first image, and the binary image is scanned row by row or column by column in the stripe direction, and the continuous lengths of bright pixels and dark pixels are saved in a length segment.

In the second step, based on the spatial distribution law that the width and spacing of light and dark stripes in the interference fringes of light are roughly the same and the number of stripes is generally large, the part of the dark stripes in the length segment is determined, restored to the pixel position of the original image and marked in a marking matrix of the same size as the original image.

The third step is to find similar points within a certain distance around the marked points in the marking matrix. These similar points have the characteristics of similar and continuous grayscale values as the marked points, so that most areas of the dark stripes can be identified.

Region merging process: The dark stripe region obtained by merging the binary images segmented by global threshold segmentation and local threshold segmentation is shown in FIG9 , which basically covers the actual dark stripe region.

Stripe elimination process: For the above-mentioned merged dark stripe area, the grayscale value of the stripe area is adjusted by the global threshold and the local threshold to eliminate the influence of the stripe. The formula for adjusting the grayscale value of the stripe area is: G = T ₁ + T ₂ - G ₀ , where T1 is the global threshold of the image, T2 is the local threshold of the image, G ₀ is the grayscale value of the original image; G is the grayscale value after transformation. The final result is shown in Figure 10. It can be seen that the influence of interference fringes on the image is basically eliminated, but due to the low contrast of the terahertz image, the image needs to be further processed.

Image enhancement process: Due to the poor quality of focal plane terahertz images and the serious and complex noise, traditional linear pull-up and histogram equalization cannot achieve the desired effect. Here we innovatively combine the local threshold segmentation results to improve the image contrast. The grayscale of the dark stripe area of the background pixels of the third image is increased, and the grayscale of the rest is reduced. Finally, the noise is reduced by mean filtering. The final result is shown in Figure 11. While greatly eliminating the influence of stripes, the actual shape of the blade can be seen more clearly in the image, and the intuitive visual effect is greatly improved, which is impossible to obtain by all filtering methods.

The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims (9)

1. A method for identifying and processing interference fringe areas in focal plane terahertz imaging, characterized in that it comprises the following steps: Performing global threshold segmentation on the image obtained in the focal plane terahertz imaging, obtaining a global threshold of the image, and obtaining a first image; Determine whether the first image has uneven brightness: if yes, perform local threshold segmentation on the first image to obtain a second image, perform stripe recognition on the second image to obtain a dark stripe area of the second image; otherwise, perform stripe recognition on the first image to obtain a dark stripe area of the first image; the determining whether the first image has uneven brightness includes: Calculate the difference of four upper, lower, left and right boundary areas of the first image respectively, where the four upper, lower, left and right boundary areas refer to four rectangular areas close to the edge of the first image; Find the maximum difference among the four boundary regions; If it is determined that the maximum absolute value of the difference is greater than a threshold, the brightness of the first image is uneven; Performing multiple local threshold segmentations according to the uneven brightness; adjusting the global threshold when performing local threshold segmentation, and calculating the average coordinate position p ₀ of bright pixels in the first image when the maximum difference is a positive value; and calculating the average coordinate position of dark pixels in the first image when the maximum difference is a negative value; The dark pixel coordinate position is recorded as D(a,b), and the threshold in the vertical direction of the original value range U from D to p ₀ is adjusted; When this direction is horizontal, the adjustment formula is as follows:

When this direction is vertical, the adjustment formula is as follows:

Where T is the adjusted threshold, T ₀ is the global threshold, Q is the adjustment amplitude, L=0 when horizontally to the left or vertically upward, L=M when horizontally to the right, L=N when vertically downward, M, N are the length and width of the image; If there is uneven brightness, the dark stripe region of the first image and the dark stripe region of the second image are combined and stripe elimination is performed to obtain a third image; otherwise, the dark stripe region of the first image is subjected to stripe elimination to obtain a fourth image; Perform image enhancement processing on the third image or the fourth image. 2. The method for identifying and processing interference fringe areas in focal plane terahertz imaging according to claim 1, characterized in that: before performing global threshold segmentation, it also includes linear stretching of the image obtained in the focal plane terahertz imaging. 3. The method for identifying and processing interference fringe areas in focal plane terahertz imaging according to claim 1, wherein the global threshold segmentation includes using a single threshold segmentation based on the maximum between-class variance method (OTSU). 4. The method for identifying and processing interference fringe areas in focal plane terahertz imaging according to claim 1, characterized in that: performing multiple local threshold segmentations according to the uneven brightness to obtain a fifth image; performing image enhancement processing on the third image or the fourth image includes using an enhanced auxiliary binary image, and the enhanced auxiliary binary image includes: When the first image does not have uneven brightness, the enhanced auxiliary binary image uses the first image; When the first image has uneven brightness, if the local threshold segmentation is performed only once, the enhanced auxiliary binary image uses the second image; otherwise, the enhanced auxiliary binary image uses the fifth image. 5. The method for identifying and processing interference fringe areas in focal plane terahertz imaging according to claim 1, wherein when the maximum difference is a positive value, the average coordinate position of bright pixels in the first image is calculated; when the maximum difference is a negative value, the average coordinate position of dark pixels in the first image is calculated. 6. A method for identifying and processing interference fringe areas in focal plane terahertz imaging according to claim 1, characterized in that: performing image enhancement processing on the third image or the fourth image includes performing grayscale enhancement processing on pixels in dark fringe areas of background pixels of the third image or the fourth image. 7. The method for identifying and processing interference fringe areas in focal plane terahertz imaging according to claim 6, characterized in that: after the grayscale enhancement processing is performed on the third image or the fourth image, a mean filtering method is used to perform noise reduction processing. 8. A system for identifying and processing interference fringe areas in focal plane terahertz imaging, characterized in that the system comprises: A global threshold segmentation module: used for performing global threshold segmentation on the image obtained in the focal plane terahertz imaging, obtaining a global threshold of the image, and obtaining a first image; Local threshold segmentation module: determine whether the first image has uneven brightness: if yes, perform local threshold segmentation on the first image to obtain a second image, perform stripe recognition on the second image to obtain a dark stripe area of the second image; otherwise, perform stripe recognition on the first image to obtain a dark stripe area of the first image; the determination of whether the first image has uneven brightness includes: Calculate the difference of four upper, lower, left and right boundary areas of the first image respectively, where the four upper, lower, left and right boundary areas refer to four rectangular areas close to the edge of the first image; Find the maximum difference among the four boundary regions; If it is determined that the maximum absolute value of the difference is greater than a threshold, the brightness of the first image is uneven; Performing multiple local threshold segmentations according to the uneven brightness; adjusting the global threshold when performing local threshold segmentation, and calculating the average coordinate position p ₀ of bright pixels in the first image when the maximum difference is a positive value; and calculating the average coordinate position of dark pixels in the first image when the maximum difference is a negative value; The dark pixel coordinate position is recorded as D(a,b), and the threshold in the vertical direction of the original value range U from D to p ₀ is adjusted; When this direction is horizontal, the adjustment formula is as follows:

When this direction is vertical, the adjustment formula is as follows:

Where T is the adjusted threshold, T ₀ is the global threshold, Q is the adjustment amplitude, L=0 when horizontally to the left or vertically upward, L=M when horizontally to the right, L=N when vertically downward, M, N are the length and width of the image; Stripe merging and elimination module: if there is uneven brightness, the dark stripe area of the first image and the dark stripe area of the second image are merged and stripe elimination is performed to obtain a third image; otherwise, the dark stripe area of the first image is subjected to stripe elimination to obtain a fourth image. 9. The interference fringe region recognition and processing system in focal plane terahertz imaging according to claim 8, characterized in that the system further comprises: Image enhancement module: performs image enhancement processing on the third image or the fourth image.

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