CN112365424B - Local self-adaptive CLAHE-based infrared image denoising enhancement method, device and system and computer-readable storage medium - Google Patents
Local self-adaptive CLAHE-based infrared image denoising enhancement method, device and system and computer-readable storage medium Download PDFInfo
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Abstract
The invention provides an infrared image denoising enhancement method, device, system and computer readable storage medium based on local self-adaptive CLAHE, which comprises the steps of receiving an infrared image with High Dynamic Range (HDR) of an infrared detector, preprocessing and compressing to generate an infrared image with low dynamic range; performing regional blocking processing on the low dynamic range infrared image, and setting a contrast limited threshold Cliplimit on each sub-block; counting the gray level histogram of each sub-block, secondarily cutting the gray level histogram according to the Cliplimit, uniformly distributing the cut part to the whole interval, obtaining the cumulative distribution function of the gray level histogram of each sub-block, and mapping the cumulative distribution function to a designated gray level space; and (3) performing bilinear interpolation operation processing, and outputting the enhanced infrared image. Compared with the existing CLAHE algorithm, the method provided by the invention provides a new adaptive mode for setting the contrast limited threshold Cliplimit of the sub-block, can further improve the image contrast, display more detail information, and solve the problem of local noise amplification of the existing infrared image enhancement algorithm.
Description
Technical Field
The invention relates to the technical field of infrared image processing, in particular to an infrared image denoising enhancement method, an infrared image denoising enhancement system and a computer-readable storage medium based on local self-adaptive CLAHE.
Background
The limited contrast histogram equalization algorithm (CLAHE) is an improved image processing method based on the Adaptive Histogram Equalization (AHE) algorithm, and although the AHE algorithm can improve the local contrast of an image and obtain more image details, there is a noise problem of excessively amplifying the same region in the image at the same time, and the CLAHE algorithm can limit such disadvantageous amplification in some ways.
In the using process of the CLAHE algorithm, the characteristic of contrast limiting is applied to global histogram equalization, noise is restrained by limiting the contrast enhancement degree of the AHE algorithm, but the traditional CLAHE is used as an infrared image enhancement method of a local gray mapping algorithm, the contrast limiting threshold Cliplimit is selected according to the number of sub-blocks and the cut-off threshold, and the problems that partial details are not displayed or the noise of an integral image is enlarged due to excessive display and the like can be possibly caused.
In this regard, attempts have been made in the prior art to improve the specific application of the CLAHE algorithm, for example, in the application No. 201910404895.8, an automatic CLAHE image enhancement method based on discrete wavelet transform is proposed, an Auto CLAHE method of automatically setting the cut point Cliplimit is proposed,
s is the number of pixels in each block, R is the dynamic range within the block, σ is the standard deviation of the sub-blocks; average value of avg sub-block pixels, c is a smaller value. In the method for enhancing infrared image, CN107784637a, cliplimit=0.1 (K max -K min )，K max 、K min Representing a maximum and a minimum gray level in each sub-block image; the maximum and minimum values of the image can be noise points, so that the problem that local noise is too large due to improper selection can be solved because the image Cliplimit is higher than a normal value.
Prior art literature:
patent document 1: CN110136084A discrete wavelet transform-based automatic CLAHE image enhancement method, device, system and storage medium
Patent document 2: CN107784637A infrared image enhancement method
Disclosure of Invention
The invention aims to provide an infrared image denoising enhancement method based on local self-adaption CLAHE, which solves the problem of insufficient local contrast and detail strength of an infrared image and reduces local noise of the image by a method of self-adaption adjustment of a contrast limited threshold Cliplimit.
According to a first aspect of the present invention, an infrared image denoising and enhancing method based on local adaptive CLAHE is provided, which comprises the following steps:
step S1: receiving an infrared image with High Dynamic Range (HDR) input by an infrared detector, and performing image preprocessing;
step S2: image compression, namely compressing an infrared image with a high dynamic range into an infrared image with a low dynamic range;
step S3: performing regional blocking processing on the compressed low dynamic range infrared image, and setting a contrast limited threshold Cliplimit on each sub-block;
step S4: counting the gray level histogram of each sub-block, secondarily cutting the gray level histogram according to the contrast limited threshold Cliplimit determined in the step S3, uniformly distributing the cut part to the whole interval, obtaining the cumulative distribution function of the gray level histogram of each sub-block, and mapping the cumulative distribution function to a designated gray level space;
step S5: and carrying out bilinear interpolation operation on the image according to the pixel positions and the gray mapping function of each region, and outputting the enhanced low dynamic range infrared image.
Preferably, in the step S3, the setting of the contrast limited threshold Cliplimit includes the following operations:
step S31: ordering all pixels in each sub-block from high to low;
step S32: selecting a gray value of a theta 2 < lambda > -img_bit position after sequencing as a contrast limited threshold Cliplimit of each sub-block, wherein 2 < lambda > -img_bit represents the dynamic range of an image, img_bit is the image bit number of the image, theta is a cut-off coefficient, and the range is [0,1];
step S33: threshold judgment is performed on the contrast limited threshold Cliplimit of the sub-block set in step S32, if the set contrast limited threshold Cliplimit is smaller than Cliplimit min Will be in Cliplimit min As the contrast limited threshold Cliplimit, otherwise, keeping the contrast limited threshold Cliplimit set in step S32 unchanged;
wherein Cliplimit min Representing the minimum limit of the contrast limited threshold, cliplimit min ＝floor(0.1*2^img_bit)。
Preferably, in the step S3, the truncated coefficient theta is 0.05, 0.1 or 0.15, which is used for controlling and adjusting the positions after sorting from large to small. Thereby changing the gradation value corresponding to the Cliplimt of each block.
Preferably, the step S4 performs histogram processing, and the specific operations thereof include the following procedures:
step S401: obtaining the sum total Excess of partial histograms higher than the contrast limited threshold Cliplimit in the histogram according to the contrast limited threshold Cliplimit of each sub-block determined in the step S3, wherein the total Excess is equally divided into all gray levels N, N=2-img_bit so as to obtain the overall rising height avgBinIncr, avgBinIncr =total Excess/N of the histogram; the histogram is treated as follows, bounded by Upper:
(1) If the gray value is higher than Upper, setting the gray value as Cliplimit;
(2) If the gray value is lower than Upper, directly filling the avgBinIncr gray values; wherein ipper=cliplimit-L;
after the above operation, the gray value from the step (1) having the gray value between Upper and Cliplimit is remained, which directly fills the value < avgbinnincr; the number of the pixel points used for filling is smaller than the total Excesses, the rest pixel points are not separated out, and the rest pixel points which are not separated out are uniformly distributed to gray values of which the amplitude is still smaller than the contrast limited threshold Cliplimit;
step S402: obtaining a corresponding histogram cumulative distribution function cdf 'according to the cut gray level histogram' k And mapped to a designated gray space, wherein the gray mapping function is:
wherein P is k As grey mapping function, cdf' max Is the maximum of the cumulative histogram distribution.
According to a second aspect of the present invention, there is provided an infrared image denoising and enhancing apparatus based on local adaptive CLAHE, comprising:
a module for receiving a High Dynamic Range (HDR) infrared image input by the infrared detector and performing image preprocessing;
a module for image compression arranged to compress the high dynamic range infrared image to a low dynamic range infrared image;
the module is used for carrying out regional blocking processing on the compressed low dynamic range infrared image and setting a contrast limited threshold Cliplimit on each sub-block;
a module for histogram processing, configured to count the gray level histogram of each sub-block, secondarily clipping the gray level histogram according to the contrast limited threshold Cliplimit determined in the step, uniformly distributing the clipped part to the whole interval, obtaining a cumulative distribution function of the gray level histogram of each sub-block, and mapping the cumulative distribution function to a designated gray level space;
and the module is used for carrying out bilinear interpolation operation on the image according to the pixel positions and the gray mapping function of each region and outputting the enhanced low dynamic range infrared image.
Preferably, the setting of the contrast limited threshold Cliplimit includes the following operations:
ordering all pixels in each sub-block from high to low;
selecting a gray value of a theta 2 < lambda > img_bit position after sequencing as a contrast limited threshold Cliplimit of each sub-block, wherein 2 < lambda > img_bit represents the dynamic range of an image, img_bit represents the image bit number of the image, theta represents a cut-off coefficient, and the range is [0,1], and m and n represent the length and the width of the sub-block respectively; and
threshold judgment is carried out on the contrast limited threshold Cliplimit of the sub-block, if the set contrast limited threshold Cliplimit is smaller than the Cliplimit min Will be in Cliplimit min As the contrast limited threshold Cliplimit, otherwise, keeping the contrast limited threshold Cliplimit set in step S32 unchanged;
wherein Cliplimit min Representing the minimum limit of the contrast limited threshold, cliplimit min ＝floor(0.1*2^img_bit)。
According to a third aspect of the object of the invention, a computer system is proposed, comprising:
one or more processors;
a memory storing instructions operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing the processes of the aforementioned methods.
According to a fourth aspect of the object of the present invention a computer readable medium storing software comprising instructions executable by one or more computers which when executed by the one or more computers perform the process of the aforementioned method is presented.
Compared with the prior art, the technical scheme of the invention has the remarkable beneficial effects that:
compared with the existing CLAHE algorithm, the optimized adaptive CLAHE infrared image enhancement method provides a new adaptive mode for setting the contrast limited threshold Cliplimit of the sub-block, and the new Cliplimit is simple and efficient in setting, can further improve image contrast and display more detail information, and solves the problems of insufficient local contrast and detail intensity and local noise amplification of the existing infrared image enhancement algorithm.
According to the invention, all pixels in each sub-block are ordered from high to low, the gray value of the theta < 2 > -img_bit position after the ordering is selected as the contrast limited threshold Cliplimit of the sub-block, and the minimum limit value is judged, so that the Cliplimit of each sub-block is changed according to the gray value condition of each block. Compared with the traditional CLAHE, after the traditional partitioning obtains the target cut-off threshold value and the target sub-image region number, the Cliplimit of each sub-block is fixed, so that the problem of noise introduction is easily caused. In the optimized local self-adaptive CLAHE infrared image enhancement method provided by the invention, if smaller Cliplimit is given to a darker area according to a blocking area, the image contrast can be further improved, more part of detail information is displayed, and meanwhile, the problem of local noise amplification of the existing infrared image enhancement algorithm is solved. Meanwhile, compared with other automatic CLAHEs, the method is simple and effective, and the efficiency can be improved.
In combination with the implementation of the invention, compared with the CLAHE enhancement method in the comparison documents 1 and 2 in the prior art, the Cliplimit setting of the method provided by the invention is simpler and more reasonable, and the efficiency can be improved.
It should be understood that all combinations of the foregoing concepts, as well as additional concepts described in more detail below, may be considered a part of the inventive subject matter of the present disclosure as long as such concepts are not mutually inconsistent. In addition, all combinations of claimed subject matter are considered part of the disclosed inventive subject matter.
The foregoing and other aspects, embodiments, and features of the present teachings will be more fully understood from the following description, taken together with the accompanying drawings. Other additional aspects of the invention, such as features and/or advantages of the exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of the embodiments according to the teachings of the invention.
Drawings
The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a fast infrared denoising enhancement method based on a local adaptive CLAHE according to an exemplary embodiment of the present invention;
FIG. 2 is a low dynamic infrared image of the stage after compression obtained in step S1;
FIG. 3 is a flow chart of adaptive adjustment Cliplimit in an exemplary embodiment of the present invention;
fig. 4 is a CLAHE-processed image adaptively optimized for low dynamic infrared images, theta=0.1;
FIG. 5 is an image of a low dynamic infrared image after CLAHE processing; in the traditional CLAHE, the Cliplimit is a fixed value, and the Cliplimit is an average value, wherein the Cliplimit=48;
FIG. 6 is an image of a low dynamic infrared image after CLAHE processing; in the conventional CLAHE, cliplimit is a fixed value, and cliplimit=89;
FIG. 7 is an image of a low dynamic infrared image after CLAHE processing; and adopting the self-adaptive optimized CLAHE processed image of the Cliplimit setting corresponding to the comparison document 1 in the prior art.
Detailed Description
For a better understanding of the technical content of the present invention, specific examples are set forth below, along with the accompanying drawings.
Aspects of the invention are described in this disclosure with reference to the drawings, in which are shown a number of illustrative embodiments. The embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those described in more detail below, may be implemented in any of a number of ways, as the disclosed concepts and embodiments are not limited to any implementation. Additionally, some aspects of the disclosure may be used alone or in any suitable combination with other aspects of the disclosure.
According to the invention, an infrared image denoising enhancement method based on local self-adaption CLAHE is provided, and the problem of insufficient local contrast and detail intensity of an infrared image is solved and local noise of the image is reduced by a method for self-adaption adjustment of Cliplimit. Preferably, in order to prevent the situation that the Cliplimit is too small (e.g., cliplimit=0), the present invention further proposes an operation of setting the contrast limited threshold Cliplimit of the sub-block in a dynamic adaptive manner.
The infrared image denoising enhancement method based on the local adaptive CLAHE in combination with the example shown in FIG. 1 comprises the following steps:
step S1: receiving an infrared image with High Dynamic Range (HDR) input by an infrared detector, and performing image preprocessing;
step S2: image compression, namely compressing an infrared image with a high dynamic range into an infrared image with a low dynamic range;
step S3: performing regional blocking processing on the compressed low dynamic range infrared image, and setting a contrast limited threshold Cliplimit on each sub-block;
step S4: counting the gray level histogram of each sub-block, secondarily cutting the gray level histogram according to the contrast limited threshold Cliplimit determined in the step S3, uniformly distributing the cut part to the whole interval, obtaining the cumulative distribution function of the gray level histogram of each sub-block, and mapping the cumulative distribution function to a designated gray level space;
step S5: and carrying out bilinear interpolation operation on the image according to the pixel positions and the gray mapping function of each region, and outputting the enhanced low dynamic range infrared image.
An exemplary implementation of the above method is described in more detail below with reference to the accompanying drawings.
In step S1, for an infrared image acquired by the acquired infrared detector, that is, an infrared image with a High Dynamic Range (HDR), preprocessing is first performed, where the preprocessing includes at least one of non-uniformity correction, blind pixel replacement, median filtering, and image noise reduction for the infrared image.
In the exemplary embodiment of the invention, blind pixel replacement processing is adopted to remove dead pixels.
In step S2, the high-dynamic to low-dynamic processing of the preprocessed image may be implemented based on an existing method. For example, the single-platform histogram equalization method performs compression processing on an infrared image, and as an example, the specific implementation process includes:
(1) Counting a gray level histogram of the infrared image;
(2) Cutting off the part exceeding a preset threshold value in the gray level histogram;
(3) Counting the cumulative histogram distribution of gray scales and mapping the cumulative histogram distribution to a target gray scale space;
(4) The high dynamic range infrared image is mapped to the low dynamic range according to the mapping function and output.
Referring to fig. 1, 2 and 3, in step 3, the compressed low dynamic range infrared image is subjected to area blocking; setting a contrast limited threshold Cliplimit for each sub-block, an exemplary process flow diagram is shown in FIG. 3, and specifically includes the following steps:
step S201: dividing the image into a plurality of areas with the same size; in this embodiment, an image having a size of 640×512 is divided into 80 areas 10×8 in total. Preferably, the subblocks have a length m of 64 and a width n of 64;
step S202: setting Cliplimit specifically comprises: and sequencing all pixels in each sub-block from high to low, and selecting the gray value of the theta 2-img_bit position after sequencing as the contrast limited threshold Cliplimit of the sub-block, wherein 2-img_bit represents the dynamic range of an image, img_bit is the number of image bits, theta is a cut-off coefficient, and the range is [0,1].
Preferably, the cutoff coefficient theta is selected to be 0.05, 0.10 and 0.15, and the positions after sorting from large to small can be controlled so as to change the gray value corresponding to Cliplimt of each block. In the illustrated example of the invention, the truncated coefficient theta is chosen to be 0.1.
In order to prevent the situation that the Cliplimit is too small (e.g., cliplimit=0), the operation of setting the contrast limited threshold Cliplimit of the sub-block in the dynamic adaptive manner according to the present invention further includes:
threshold judgment is carried out on the contrast limited threshold Cliplimit of the sub-block, if the set contrast limited threshold Cliplimit is smaller than the Cliplimit min Will be in Cliplimit min As the contrast limited threshold Cliplimit, otherwise, keeping the contrast limited threshold Cliplimit set in step S32 unchanged;
wherein Cliplimit min Representing the minimum limit of the contrast limited threshold, cliplimit min ＝floor(0.1*2^img_bit)。
In the illustrated embodiment, img_bit=8 is taken as an example for comparison and explanation.
Thus, for a conventional CLAHE, the Cliplimit is selected from the 10×8 region; each sub-block is provided with a unified clipping threshold Cliplimit as Cliplimit mean As a comparison; shown in fig. 5.
For the traditional CLAHE, selecting a 10 multiplied by 8 area of the Cliplimit; each sub-block is provided with a unified clipping threshold Cliplimit as Cliplimit max As a comparison; shown in fig. 6.
Cliplimit setting in the CLAHE enhancement method for the method described in comparative document 1 is used as a comparison. Wherein, the liquid crystal display device comprises a liquid crystal display device,
s is the number of pixels in each block, R is the dynamic range within the block, σ is the standard deviation of the sub-blocks; the average value of avg sub-block pixels, c, is a smaller value and the processing result is shown in fig. 7.
The traditional CLAHE method can set a unified clipping threshold value for each sub-block; in the improved self-adaptive infrared image enhancement, the contrast limited threshold Cliplimit of each sub-block is different, so that the contrast can be further improved, and details can be displayed; meanwhile, when the sub-blocks are smoother, the gray level in the statistical histogram is concentrated, the histogram in the sub-block of the smooth area in the traditional CLAHE can be cut off more pixel numbers, at the moment, the cutting threshold value of the smooth area can be automatically adjusted and reduced through the self-adaptive Cliplimit selection method, the noise space in the block area is effectively compressed, and the purpose of noise reduction is achieved.
In step S4, the gray level histogram of each block area is counted by histogram processing, the histogram of each block area is cut, the gray level histogram is cut according to the Cliplimit cutting threshold of each sub-block determined in step S2, and the cut-out parts are uniformly distributed to the whole section.
In an alternative manner, the following operations are specifically included:
step S401: obtaining the sum total Excess of partial histograms higher than the contrast limited threshold Cliplimit in the histogram according to the contrast limited threshold Cliplimit of each sub-block determined in the step S3, wherein the total Excess is equally divided into all gray levels N, N=2-img_bit so as to obtain the overall rising height avgBinIncr, avgBinIncr =total Excess/N of the histogram; the histogram is treated as follows, bounded by Upper:
(1) If the gray value is higher than Upper, setting the gray value as Cliplimit;
(2) If the gray value is lower than Upper, directly filling the avgBinIncr gray values; wherein ipper=cliplimit-L;
after the above operation, the gray value from the step (1) having the gray value between Upper and Cliplimit is remained, which directly fills the value < avgbinnincr; the number of the pixel points used for filling is smaller than the total Excesses, the rest pixel points are not separated out, and the rest pixel points which are not separated out are uniformly distributed to gray values of which the amplitude is still smaller than the contrast limited threshold Cliplimit;
step S402: obtaining a corresponding histogram cumulative distribution function cdf 'according to the cut gray level histogram' k And mapped to a designated gray space, wherein the gray mapping function is:
wherein P is k As grey mapping function, cdf' max Is the maximum of the cumulative histogram distribution.
And after each sub-block is subjected to histogram processing, outputting the enhanced low dynamic range infrared image through linear interpolation. In alternative embodiments, the linear interpolation process may be implemented in existing ways, particularly by bilinear interpolation algorithms.
With reference to the examples shown in fig. 4-6, fig. 4 is an image after the low-dynamic infrared image is subjected to the CLAHE processing after the adaptive optimization, and fig. 5 and 6 are corresponding images after the conventional CLAHE enhancement processing, where cliplimit=48 in fig. 5 and cliplimit=89 in fig. 6, respectively.
As can be seen from the comparison of the diagrams, the conventional CLAHE method can realize the enhancement of the image on a certain program, but simultaneously accompanies the problem of local noise amplification, and the following table is obtained by calculating the PSNR (peak signal to noise ratio) of the image:
combining the comparison test results, wherein one is to compare with the CLAHE enhancement algorithm for fixing Cliplimit (Cliplimit=48; 89); the other is that the auto CLAHE enhancement algorithm corresponding to the prior art of the background 1 is compared with the fixed Cliplimit, and the noise is smaller.
The table shows that the PSNR of the image is obviously improved after the CLAHE is processed by the method, and the infrared image enhancement method for optimizing the CLAHE has obvious improvement on the background noise of the image.
The contrast effect of the image processed by the prior art patent 1 is basically the same, but is simpler and faster than that of the prior art patent 1. In prior art patent 1
The calculation of Cliplimit requires the number of pixels S in each block, the dynamic range R in the block, and the standard deviation sigma of sub-blocks; average avg of sub-block pixels, smaller value c. While the algorithm herein only requires sorting from high to low and selecting the order for all pixels in each sub-blockThe gray value at the post theta 2 x 2 img_bit position is used as the contrast limited threshold Cliplimit of the sub-block. The required calculation elements are greatly reduced, and the method is more suitable for being applied to the FPGA.
The following table is an example of the value of the Cliplimit of each sub-block (10×8 sub-blocks) of the low-dynamic infrared image after the self-adaptive optimization of the present invention, and gives the value table of the Cliplimit in the 10×8 blocks obtained by the program in the implementation example. According to the comparison, each sub-block of the traditional CLAHE is compared with a fixed value, so that the Cliplimit value of each sub-block is different and is changed according to the change of the integral gray value of the sub-block, the image is clearer, the image contrast can be further improved, more part of detail information is displayed, and meanwhile, the problem of local noise amplification of the existing infrared image enhancement algorithm is solved.
The fast infrared image denoising enhancement method based on adaptive CLAHE according to the present invention can also be configured to be implemented in the following manner, in conjunction with the description of the above figures and embodiments.
Infrared image denoising and enhancing device based on local self-adaptive CLAHE
In an exemplary embodiment, an infrared image denoising enhancement apparatus based on a local adaptive CLAHE, comprising:
a module for receiving a High Dynamic Range (HDR) infrared image input by the infrared detector and performing image preprocessing;
a module for image compression arranged to compress the high dynamic range infrared image to a low dynamic range infrared image;
the module is used for carrying out regional blocking processing on the compressed low dynamic range infrared image and setting a contrast limited threshold Cliplimit on each sub-block;
a module for histogram processing, configured to count the gray level histogram of each sub-block, secondarily clipping the gray level histogram according to the contrast limited threshold Cliplimit determined in the step, uniformly distributing the clipped part to the whole interval, obtaining a cumulative distribution function of the gray level histogram of each sub-block, and mapping the cumulative distribution function to a designated gray level space;
and the module is used for carrying out bilinear interpolation operation on the image according to the pixel positions and the gray mapping function of each region and outputting the enhanced low dynamic range infrared image.
Wherein, the setting of the contrast limited threshold Cliplimit includes the following operations:
ordering all pixels in each sub-block from high to low;
in each sub-block, selecting the gray value of the ordered theta 2 < lambda > -img_bit position as the contrast limited threshold Cliplimit of the sub-block, wherein 2 < lambda > -img_bit represents the dynamic range of the image, img_bit represents the number of bits of the image, theta represents the truncated coefficient, and the range is [0,1], and
threshold judgment is carried out on the contrast limited threshold Cliplimit of the sub-block, if the set contrast limited threshold Cliplimit is smaller than the Cliplimit min Will be in Cliplimit min As the contrast limited threshold Cliplimit, otherwise, keeping the contrast limited threshold Cliplimit set in step S32 unchanged;
wherein Cliplimit min Representing the minimum limit of the contrast limited threshold, cliplimit min ＝floor(0.1*2^img_bit)。
Wherein the means for histogram processing is arranged to perform histogram processing and gray space mapping in the following manner:
firstly, according to a determined contrast limited threshold Cliplimit of each sub-block, obtaining the sum total Excess of partial histograms higher than the contrast limited threshold Cliplimit in the histogram, and at the moment, assuming that the total Excess are equally divided into all gray levels N, N=2 heat_bit so as to obtain the overall rising height avgBinIncr, avgBinIncr =total Excess/N of the histogram; the histogram is treated as follows, bounded by Upper:
(1) If the gray value is higher than Upper, setting the gray value as Cliplimit;
(2) If the gray value is lower than Upper, directly filling the avgBinIncr gray values; wherein ipper=cliplimit-L;
after the above operation, the gray value from the step (1) having the gray value between Upper and Cliplimit is remained, which directly fills the value < avgbinnincr; the number of the pixel points used for filling is smaller than the total Excesses, the rest pixel points are not separated out, and the rest pixel points which are not separated out are uniformly distributed to gray values of which the amplitude is still smaller than the contrast limited threshold Cliplimit;
then, a corresponding histogram cumulative distribution function cdf 'is obtained according to the cut gray level histogram' k And mapped to a designated gray space, wherein the gray mapping function is:
wherein P is k As grey mapping function, cdf' max Is the maximum of the cumulative histogram distribution.
Computer system
In an exemplary embodiment, a computer system includes:
one or more processors;
a memory storing instructions operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing the foregoing method, particularly the process of the method shown in fig. 1.
Computer readable medium
In an exemplary embodiment, a computer readable medium storing software comprising instructions executable by one or more computers which when executed by the one or more computers perform the foregoing method, particularly the process of the method shown in fig. 1.
While the invention has been described with reference to preferred embodiments, it is not intended to be limiting. Those skilled in the art will appreciate that various modifications and adaptations can be made without departing from the spirit and scope of the present invention. Accordingly, the scope of the invention is defined by the appended claims.
Claims (7)
1. The local self-adaptive CLAHE-based infrared image denoising and enhancing method is characterized by comprising the following steps of:
step S1: receiving an infrared image with high dynamic range input by an infrared detector, and performing image preprocessing;
step S2: image compression, namely compressing an infrared image with a high dynamic range into an infrared image with a low dynamic range;
step S3: performing regional blocking processing on the compressed low dynamic range infrared image, and setting a contrast limited threshold Cliplimit on each sub-block;
step S4: counting the gray level histogram of each sub-block, secondarily cutting the gray level histogram according to the contrast limited threshold Cliplimit determined in the step S3, uniformly distributing the cut part to the whole interval, obtaining the cumulative distribution function of the gray level histogram of each sub-block, and mapping the cumulative distribution function to a designated gray level space;
step S5: carrying out bilinear interpolation operation on the image according to the pixel position and the gray mapping function of each region, and outputting an enhanced low dynamic range infrared image;
wherein, in the step S3, the setting of the contrast limited threshold Cliplimit includes the following operations:
step S31: ordering all pixels in each sub-block from high to low;
step S32: selecting a gray value of a theta 2 < lambda > img_bit position after sequencing as a contrast limited threshold Cliplimit of each sub-block, wherein 2 < lambda > img_bit represents the dynamic range of the sub-block, img_bit represents the image bit number of the sub-block, theta represents a cut-off coefficient, and the range is [0,1], and m and n represent the length and the width of the sub-block respectively;
step S33: thresholding the contrast limited threshold Cliplimit of the sub-block set in step S32Value determination, if the set contrast limited threshold Cliplimit is smaller than Cliplimit min Will be in Cliplimit min As the contrast limited threshold Cliplimit, otherwise, keeping the contrast limited threshold Cliplimit set in step S32 unchanged;
wherein Cliplimit min Representing the minimum limit of the contrast limited threshold, cliplimit min = floor(0.1*2^img_bit)。
2. The local adaptive CLAHE-based infrared image denoising enhancement method according to claim 1, wherein in the step S3, each sub-block is obtained for the regional block, and a dynamic adaptive mode is adopted to set a contrast limited threshold Cliplimit of the sub-block.
3. The local adaptive CLAHE-based infrared image denoising enhancement method according to claim 1, wherein in the step S3, the truncated coefficient theta takes a value of 0.05, 0.1 or 0.15, which is used for controlling and adjusting the positions after sorting from large to small.
4. The local adaptive CLAHE-based infrared image denoising enhancement method as claimed in claim 1, wherein the preprocessing comprises at least one of non-uniformity correction, blind pixel replacement or image denoising of the infrared image.
5. An infrared image denoising and enhancing device based on local self-adaptive CLAHE, which is characterized by comprising:
the module is used for receiving the high dynamic range infrared image input by the infrared detector and preprocessing the image;
a module for image compression arranged to compress the high dynamic range infrared image to a low dynamic range infrared image;
the module is used for carrying out regional blocking processing on the compressed low dynamic range infrared image and setting a contrast limited threshold Cliplimit on each sub-block;
a module for histogram processing, configured to count the gray level histogram of each sub-block, secondarily clipping the gray level histogram according to the contrast limited threshold Cliplimit determined in the step, uniformly distributing the clipped part to the whole interval, obtaining a cumulative distribution function of the gray level histogram of each sub-block, and mapping the cumulative distribution function to a designated gray level space;
the module is used for carrying out bilinear interpolation operation on the image according to the pixel positions and the gray mapping function of each region and outputting an enhanced low dynamic range infrared image;
the module for performing area blocking processing on the compressed low dynamic range infrared image and performing setting of the contrast limited threshold Cliplimit on each sub-block is set to perform setting of the contrast limited threshold Cliplimit on each sub-block in the following manner:
step S31: ordering all pixels in each sub-block from high to low;
step S32: selecting a gray value of a theta 2 < lambda > img_bit position after sequencing as a contrast limited threshold Cliplimit of each sub-block, wherein 2 < lambda > img_bit represents the dynamic range of the sub-block, img_bit represents the image bit number of the sub-block, theta represents a cut-off coefficient, and the range is [0,1], and m and n represent the length and the width of the sub-block respectively;
step S33: threshold judgment is performed on the contrast limited threshold Cliplimit of the sub-block set in step S32, if the set contrast limited threshold Cliplimit is smaller than Cliplimit min Will be in Cliplimit min As the contrast limited threshold Cliplimit, otherwise, keeping the contrast limited threshold Cliplimit set in step S32 unchanged;
wherein Cliplimit min Representing the minimum limit of the contrast limited threshold, cliplimit min = floor(0.1*2^img_bit)。
6. A computer system, comprising:
one or more processors;
a memory storing instructions operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing the process of any of the methods of claims 1-4.
7. A computer readable medium storing software, wherein the software comprises instructions executable by one or more computers which when executed by the one or more computers perform the process of the method of any one of claims 1-4.
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