CN117392031A - Multi-aperture optical panoramic image view enhancement method - Google Patents
Multi-aperture optical panoramic image view enhancement method Download PDFInfo
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Abstract
The invention discloses a multi-aperture optical panoramic image view enhancement method, which is used for realizing the purpose of monitoring a large scene target by a multi-aperture optical array image stitching technology under the condition of limited optical view field, and specifically comprises the following steps: 1) Acquiring a multi-aperture image; 2) Performing format conversion on the multi-aperture image; 3) Counting a single-aperture image histogram and a multi-aperture image histogram; 4) Calculating a calibration base value of the aperture through a single aperture image histogram; 5) Calculating segmentation threshold values of the highlight and low-light areas through the multi-aperture image histogram; 6) Calculating calibration parameters of the panoramic image; 7) And calibrating the panoramic image in real time by using the calibration parameters.
Description
Technical Field
The invention relates to a view enhancement method, in particular to a multi-aperture optical panoramic image view enhancement method.
Background
The traditional optical imaging system has narrow monitoring field, large field and high resolution, and is becoming a tripolite for limiting the rapid development of the optical imaging system. In order to effectively solve the contradiction of mutual restriction of the optical view field and the spatial resolution, a panoramic image generated by adopting multi-aperture imaging and image stitching technology can be used for ensuring wide-area monitoring and simultaneously viewing detail information of a target, so that the wide-range monitoring panoramic image is widely focused and studied by domain students. Ming-Shing Su et al uses wavelets for multi-scale fusion, however, this approach only performs local fusion processing in the overlapping region, and the global nature of the image is not guaranteed. An Ant Levin et al adopts a gradient domain fusion method to splice images, so that the influence of original image chromatic aberration on fusion results is reduced, but for images with obvious color and brightness differences, panoramic images are distorted. The existing panoramic image generation method has the following defects: (1) For images with obvious color and brightness differences, the distortion degree of the panoramic image generated by splicing is larger; (2) In general, no obvious seam between adjacent images can not be ensured; (3) Most panoramic image generation algorithms are complex in operation and high in hardware resource consumption.
Disclosure of Invention
The invention aims to: the invention aims to solve the technical problem of providing a multi-aperture optical panoramic image view enhancement method aiming at the defects of the prior art.
In order to solve the technical problems, the invention discloses a multi-aperture optical panoramic image view enhancement method, which comprises the following steps:
step 1: acquiring a multi-aperture image;
step 2: performing format conversion on the multi-aperture image, and unifying all the images into a gray level image format;
step 3: counting histograms of the single-aperture image and the multi-aperture image;
step 4: calculating a calibration basic value of each single-aperture image through the single-aperture image histogram;
step 5: calculating segmentation threshold values of the highlight and low-light areas through the multi-aperture image histogram;
step 6: calculating the calibration parameters of the panoramic image according to the calibration basic values and the segmentation threshold values of the apertures; the panoramic image is synthesized by multi-aperture images;
step 7: and carrying out real-time calibration on the panoramic image by using the calibration parameters, and completing the visual enhancement of the multi-aperture optical panoramic image.
Further, the multi-aperture image in the step 1 refers to the optical image obtained by the number of visible light cameras or thermal infrared imagers being more than or equal to 4; the image types include at least: visible or infrared images.
Further, the format conversion in step 2 includes:
converting the format of the multi-aperture image: if the original multi-aperture image is in RGB format, converting it into YUV format; if the original multi-aperture image is a gray scale image, it remains unchanged.
Further, the histogram described in step 3, namely: the abscissa is the luminance value r, and the ordinate is the probability of occurrence of the luminance value r.
Further, in the histogram of the single-aperture image described in step 3, the method includes:
assume that the single aperture image is Img 1 ，…，Img k ，Img k (i, j) is the image Img of the kth aperture k Luminance value, p, of middle pixel point (i, j) r (Img k ) Is an image Img k The probability of occurrence of a pixel with a luminance value r is:
wherein k represents the number of single-aperture images in the multi-aperture image, and (i, j) is the pixel position, n r (Img k ) Is an image Img k The number of pixels having a luminance value r, n (Img k ) Is an image Img k Is a total number of pixels.
Further, in the histogram of the multi-aperture image in step 3, the method includes:
assuming that the porous image is Img, img (i, j) is the luminance value of the pixel (i, j) in the porous image Img, p r (Img) is the probability of occurrence of a pixel point with a luminance value r of the image Img, then
Where (i, j) is the pixel position, n r (Img) is the number of pixels having a luminance value r, and n (Img) is the total number of pixels.
Further, in the step 4, calculating the calibration base value of each single-aperture image through the single-aperture image histogram specifically includes:
step 4-1, setting an image Img k Histogram His of (1) k The high and low proportionality coefficients are the same, the high and low proportionality coefficients are preset values, and the image Img of the kth aperture k The range of the luminance value r is 0,255]；
Step 4-2, calculating a high threshold T k,high And a low threshold T k，low The method is characterized by comprising the following steps:
said high threshold T k，high The calculation method comprises the following steps:
when the brightness value is within the value range [ r,255]The number of pixels is equal to the preset value multiplied by the image Img k The total number of pixels n (Img) k ) At this time, the brightness value r is a high threshold T k，high ；
Said low threshold T k，low The calculation method comprises the following steps:
when the brightness value is within the value range [0, r]The number of pixels is equal to the preset value multiplied by the image Img k The total number of pixels n (Img) k ) At this time, the brightness value r is a low threshold T k，low ；
Step 4-3, calculating a calibration basic value of each single-aperture image, wherein the specific method comprises the following steps:
image Img k Is JzJ of the calibration base value of (2) k Is brightness value greater than T k，low And is less than T k，hig Is a pixel mean value of (c).
Further, the calculating the segmentation threshold of the highlight and the highlight areas in the step 5 specifically includes:
step 5-1, setting the high-low proportionality coefficient of the histogram His of the multi-aperture image Img to be the same as the high-low proportionality coefficient set in step 4-1;
step 5-2, calculating a high threshold T by adopting the method of step 4-2 hig And a low threshold T low ；
Step 5-3, calculating that the brightness value in the multi-aperture image Img is larger than T according to the threshold segmentation theory low And is less than T high The segmentation threshold Thr for the highlight and the highlight regions of (c).
Further, the calculating the calibration parameters of the panoramic image in step 6 specifically includes:
in the panoramic image, the calibration base value JzG of the highlight region is a luminance value greater than T low And a pixel mean value less than Thr; the calibration base value JzD for the low-light region is a luminance value greater than Thr and less than T hig Is a pixel mean value of (2);
calibration parameters Jz of panoramic image k Expressed as:
where k represents the kth aperture.
Further, the calibrating the panoramic image in real time by using the calibration parameters in step 7 specifically includes:
kth single aperture image Img k Is shown in (a) is a calibration image Img' k Expressed as:
Img′ k ＝Img k -Jz k
and after all the single-aperture images are calibrated through the above method, outputting a calibrated panoramic image through image stitching.
The beneficial effects are that:
(1) For images with obvious color and brightness differences, the distortion degree of the panoramic image generated by splicing is small, the detail information of the images is rich and remarkable, and the target monitoring of a user is facilitated;
(2) The color transition between adjacent images is smooth, no obvious splicing gap exists, and the visual perception is good;
(3) The panoramic image view enhancement method provided by the invention has the advantages that no higher-order operation exists, the algorithm structure is simple, and the real-time monitoring of a high-speed target is facilitated.
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The foregoing and/or other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings and detailed description.
Fig. 1 is a schematic of the workflow of the present invention.
FIG. 2 is a schematic diagram of image stitching effects in one embodiment of the invention.
Detailed Description
The invention provides a multi-aperture optical panoramic image view enhancement method, which is used for carrying out fusion splicing and image enhancement on multiple paths of optical sensor images, providing a wide scene panoramic image and meeting the user requirements of large scene target monitoring, and mainly comprises the following steps: acquiring a multi-aperture image, wherein the multi-aperture image refers to images output by a plurality of optical lenses; performing format conversion on the multi-aperture image; counting a single-aperture image histogram and a multi-aperture image histogram, wherein the single-aperture image refers to an image output by a single optical lens; calculating a calibration base value of the aperture through a single aperture image histogram; calculating segmentation threshold values of the highlight and low-light areas through the multi-aperture image histogram; calculating calibration parameters of the panoramic image; and carrying out real-time calibration on the panoramic image by using the calibration parameters, wherein the method comprises the following steps:
step 1: acquiring a multi-aperture image;
the multi-aperture image means that the number of visible light cameras or thermal infrared imagers is more than or equal to 4; image types include visible light, infrared images, and the like.
The visible light image refers to a high definition (2K) or ultra-high definition (4K) RGB color image; the infrared image refers to a gray image of 320×256, 620×512, or 1024×1024.
Step 2: performing format conversion on the multi-aperture image;
converting the format of the multi-aperture image: if the original image type is in RGB format, converting it into YUV format; if the original image type is a gray scale image, it remains unchanged.
The formula for converting the RGB format of the color image into the YUV format is as follows:
Y＝0.2990R+0.5870G+0.1440B
U＝-0.1684R-0.3316G+0.5B+128
V＝0.5R-0.4187G-0.0813B+128
step 3: counting a single-aperture image histogram and a multi-aperture image histogram;
assume that the single aperture image is Img 1 ，…，Img k ，Img k (i, j) is an image Img k Luminance value, p, of pixel (i, j) r (Img k ) Is an image Img k The luminance value is the probability of r occurrence
Where k represents the number of images with multiple apertures, (i, j) is the pixel position, n r (Img k ) Is an image Img k The number of pixels having a luminance value r, n (Img k ) Is an image Img k Is a total number of pixels.
Assuming that the multi-aperture image is Img, img (i, j) is the luminance value of the image Img pixel (i, j), p r (Img) is the probability that the luminance value r of the image Img appears, then
Where (i, j) is the pixel position, n r (Img) is the number of pixels having a luminance value r, and n (Img) is the total number of pixels.
Step 4: calculating a calibration base value of the aperture through a single aperture image histogram;
for the kth aperture, an image Img is set k Histogram His k Where we set the high and low scaling factors for each aperture to be the same, calculate the high threshold T k,high And a low threshold T k,low Image Img k Is JzJ of the calibration base value of (2) k Is brightness value greater than T k,low And is less than T k，high Is a pixel mean value of (c).
Step 5: calculating segmentation threshold values of the highlight and low-light areas through the multi-aperture image histogram;
setting high and low proportionality coefficients of a histogram His of the multi-aperture image Img, and calculating a high threshold T as the same as the high and low proportionality coefficients in the step 4 high And a low threshold T low According to the otsu adaptive threshold segmentation theory (refer to https:// zhuanlan. Zhihu. Com/p/395708037), calculating that the brightness value in Img is greater than T low And is less than T high Segmentation threshold Thr for highlight and highlight regions.
Step 6: calculating calibration parameters of the panoramic image;
the calibration base value JzG for the highlighted area of the panoramic image is a luminance value greater than T low And a pixel mean value less than Thr; the calibration base value JzD for the low-light region is a luminance value greater than Thr and less than T high Is a pixel mean value of (c).
Calibration parameters Jz of panoramic image k Expressed as:
where k represents the kth aperture.
Step 7: and calibrating the panoramic image in real time by using the calibration parameters.
Kth aperture image Img k Calibration image Img' k Expressed as:
Img′ k ＝Img k -Jz k
from the calibration image Img' k And outputting the panoramic image after image stitching.
Examples:
the invention will be further described with reference to the accompanying drawings and examples.
As shown in fig. 1, the invention discloses a multi-aperture optical panoramic image view enhancement method, which comprises the following steps:
(1) Acquiring a multi-aperture image;
here, the view enhancement method of the present invention will be specifically described by taking an example of 8-way 1920×1080 color image (RGB format).
(2) Performing format conversion on the multi-aperture image;
the formula for converting the RGB format of the color image into the YUV format is as follows:
Y＝0.2990R+0.5870G+0.1440B
U＝-0.1684R-0.3316G+0.5B+128
V＝0.5R-0.4187G-0.0813B+128
(3) Counting a single-aperture image histogram and a multi-aperture image histogram;
according to probability modelStatistics of 8 aperture images Img 1 ，…，Img 8 Is a histogram of (a) of the image.
Image Img with 1 st aperture 1 In the case of an example of this,image Img 1 The probability of occurrence of the medium luminance value r is the image Img 1 Number n of medium brightness values r r (Img 1 ) Divided by image Img 1 Total number of all luminance values n (Img 1 ) Wherein, the value range of r is [0,255]。
According to probability modelAnd counting the histogram of the multi-aperture image histogram Img. (4) Calculating a calibration base value of the aperture through a single aperture image histogram;
setting high and low scale coefficients of 8 apertures respectively, and setting an image Img for the kth aperture k Histogram His k The high and low proportionality coefficients of (2) are 0.05, taking the 1 st aperture as an example, the high threshold T 1，high Calculated as 223, low threshold T 1，low The calculation result is 76; image Img 1 Is JzJ of the calibration base value of (2) 1 Is the average value of pixels with brightness values greater than 76 and less than 223, and the calculation result is 159.
(5) Calculating segmentation threshold values of the highlight and low-light areas through the multi-aperture image histogram;
setting high and low proportionality coefficients of the multi-aperture image being the Img histogram His, setting the high and low proportionality coefficients in the step 4 to be 0.05, calculating a high threshold 241 and a low threshold 35, and calculating a segmentation threshold 178 of a high and low brightness region with a brightness value larger than 35 and smaller than 241 in the Img according to the otsu threshold segmentation theory.
(6) Calculating calibration parameters of the panoramic image;
the calibration basic value JzG of the highlight region of the panoramic image is the pixel mean value with the low brightness value being more than 35 and less than 178, and the calculation result is 142; the calibration base value JzD for the highlight region is the average of pixels with luminance values greater than 178 and less than 241, and the calculation result is 213.
Calibration parameters Jz of panoramic image k Expressed as:
where k represents the kth aperture.
(7) And calibrating the panoramic image in real time by using the calibration parameters.
For 8 apertures, the kth aperture image Img k Calibration image Img' k Expressed as:
Img′ k ＝Img k -Jz k
from the calibration image Img' k And outputting a panoramic image after image stitching, as shown in fig. 2.
In a specific implementation, the application provides a computer storage medium and a corresponding data processing unit, wherein the computer storage medium can store a computer program, and the computer program can run the invention content of the multi-aperture optical panoramic image view enhancement method and part or all of the steps in each embodiment when being executed by the data processing unit. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like.
It will be apparent to those skilled in the art that the technical solutions in the embodiments of the present invention may be implemented by means of a computer program and its corresponding general hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied essentially or in the form of a computer program, i.e. a software product, which may be stored in a storage medium, and include several instructions to cause a device (which may be a personal computer, a server, a single-chip microcomputer, MUU or a network device, etc.) including a data processing unit to perform the methods described in the embodiments or some parts of the embodiments of the present invention.
The invention provides a thought and a method for enhancing the visual field of a multi-aperture optical panoramic image, and the method and the way for realizing the technical scheme are numerous, the above description is only a preferred embodiment of the invention, and it should be noted that, for a person skilled in the art, a plurality of improvements and modifications can be made without departing from the principle of the invention, and the improvements and modifications are also considered as the protection scope of the invention. The components not explicitly described in this embodiment can be implemented by using the prior art.
Claims (10)
1. The multi-aperture optical panoramic image view enhancement method is characterized by comprising the following steps of:
step 1: acquiring a multi-aperture image;
step 2: performing format conversion on the multi-aperture image, and unifying all the images into a gray level image format;
step 3: counting histograms of the single-aperture image and the multi-aperture image;
step 4: calculating a calibration basic value of each single-aperture image through the single-aperture image histogram;
step 5: calculating segmentation threshold values of the highlight and low-light areas through the multi-aperture image histogram;
step 6: calculating the calibration parameters of the panoramic image according to the calibration basic values and the segmentation threshold values of the apertures; the panoramic image is synthesized by multi-aperture images;
step 7: and carrying out real-time calibration on the panoramic image by using the calibration parameters, and completing the visual enhancement of the multi-aperture optical panoramic image.
2. The multi-aperture optical panoramic image view enhancement method according to claim 1, wherein the multi-aperture image in step 1 is an optical image obtained by more than or equal to 4 paths of visible light cameras or thermal infrared imagers; the image types include at least: visible or infrared images.
3. The method of claim 2, wherein the format conversion in step 2 comprises:
converting the format of the multi-aperture image: if the original multi-aperture image is in RGB format, converting it into YUV format; if the original multi-aperture image is a gray scale image, it remains unchanged.
4. A multi-aperture optical panoramic image view enhancement method as recited in claim 3, wherein the histogram in step 3 is: the abscissa is the luminance value r, and the ordinate is the probability of occurrence of the luminance value r.
5. The method for enhancing the view of a multi-aperture optical panoramic image as recited in claim 4, wherein the histogram of the single-aperture image in step 3 comprises:
assume that the single aperture image is Img 1 ，…，Img k ，Img k (i, j) is the image Img of the kth aperture k Luminance value, p, of middle pixel point (i, j) r (Img k ) Is an image Img k The probability of occurrence of a pixel with a luminance value r is:
wherein k represents the number of single-aperture images in the multi-aperture image, and (i, j) is the pixel position, n r (Img k ) Is an image Img k The number of pixels having a luminance value r, n (Img k ) Is an image Img k Is a total number of pixels.
6. The method for enhancing the view of a multi-aperture optical panoramic image as recited in claim 5, wherein the histogram of the multi-aperture image in step 3 comprises:
assuming that the porous image is Img, img (i, j) is the luminance value of the pixel (i, j) in the porous image Img, p r (Img) is the probability of occurrence of a pixel point with a luminance value r of the image Img, then
Where (i, j) is the pixel position, n r (Img) is the number of pixels having a luminance value r, and n (Img) is the total number of pixels.
7. The method for enhancing the view of a multi-aperture optical panoramic image as recited in claim 6, wherein said calculating the calibration base value of each single-aperture image from the single-aperture image histogram in step 4 comprises:
step 4-1, setting an image Img k Histogram His of (1) k The high and low proportionality coefficients are the same, the high and low proportionality coefficients are preset values, and the image Img of the kth aperture k The range of the middle brightness value r is [0,255 ]]；
Step 4-2, calculating a high threshold T k,high And a low threshold T k,low The method is characterized by comprising the following steps:
said high threshold T k,high The calculation method comprises the following steps:
when the brightness value is within the value range [ r,255]The number of pixels is equal to the preset value multiplied by the image Img k The total number of pixels n (Img) k ) At this time, the brightness value r is a high threshold T k,high ；
Said low threshold T k,low The calculation method comprises the following steps:
when the brightness value is within the value range [0, r]The number of pixels is equal to the preset value multiplied by the image Img k The total number of pixels n (Img) k ) At this time, the brightness value r is a low threshold T k,low ；
Step 4-3, calculating a calibration basic value of each single-aperture image, wherein the specific method comprises the following steps:
image Img k Is JzJ of the calibration base value of (2) k Is brightness value greater than T k,low And is less than T k,high Is a pixel mean value of (c).
8. The method for enhancing the view of a multi-aperture optical panoramic image as recited in claim 7, wherein said calculating the segmentation threshold of the highlight and the highlight regions in step 5 comprises:
step 5-1, setting the high-low proportionality coefficient of the histogram His of the multi-aperture image Img to be the same as the high-low proportionality coefficient set in step 4-1;
step 5-2, calculating a high threshold T by adopting the method of step 4-2 hig And a low threshold T low ；
Step 5-3, calculating that the brightness value in the multi-aperture image Img is larger than T according to the threshold segmentation theory low And is less than T high The segmentation threshold Thr for the highlight and the highlight regions of (c).
9. The multi-aperture optical panoramic image view enhancement method of claim 8, wherein calculating the calibration parameters of the panoramic image in step 6 specifically comprises:
in the panoramic image, the calibration base value JzG of the highlight region is a luminance value greater than T low And a pixel mean value less than Thr; the calibration base value JzD for the low-light region is a luminance value greater than Thr and less than T high Is a pixel mean value of (2);
calibration parameters Jz of panoramic image k Expressed as:
where k represents the kth aperture.
10. The method for enhancing the view of a multi-aperture optical panoramic image as recited in claim 9, wherein the calibrating the panoramic image in step 7 with the calibration parameters comprises:
kth single aperture image Img k Is shown in (a) is a calibration image Img' k Expressed as:
Img′ k ＝Img k -Jz k
and after all the single-aperture images are calibrated through the above method, outputting a calibrated panoramic image through image stitching.
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