CN113112438B - Image enhancement method based on clipping histogram - Google Patents

Image enhancement method based on clipping histogram Download PDF

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CN113112438B
CN113112438B CN202110477068.9A CN202110477068A CN113112438B CN 113112438 B CN113112438 B CN 113112438B CN 202110477068 A CN202110477068 A CN 202110477068A CN 113112438 B CN113112438 B CN 113112438B
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image
histograms
histogram
clipping
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CN113112438A (en
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杨安东
张恒
范越
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723 Research Institute of CSIC
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20132—Image cropping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

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  • General Physics & Mathematics (AREA)
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  • Computer Vision & Pattern Recognition (AREA)
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Abstract

The invention discloses an image enhancement method based on a clipping histogram. The method comprises the following steps: calculating the median value of the image, and dividing the image into two sub-histograms according to the median value; respectively calculating average brightness values of the two sub-histograms; taking the average brightness value of the two sub-histograms as a segmentation threshold value, and dividing the original image into three sub-histograms; cutting each sub-histogram; and homogenizing each sub-image, and combining to obtain a complete enhanced image. The invention uses the algorithm of histogram clipping to control enhancement, suppresses noise, improves contrast, and simultaneously keeps the details of the enhanced picture good.

Description

Image enhancement method based on clipping histogram
Technical Field
The invention relates to the technical field of image enhancement, in particular to an image enhancement method based on a clipping histogram.
Background
Image enhancement technology is an important direction of research in digital image processing, and can be applied to various fields, such as: archaeology, medical image processing and analysis, photographic image processing, and the like. Image enhancement aims to enhance the brightness of an image and improve the contrast of the image while maintaining local detail, and reducing noise influence.
The histogram equalization HE is widely used as a classical image enhancement algorithm due to its simplicity and ease of implementation. HE uniformly distributes the input image pixel mean values, which may cause excessive differences between the average brightness of the image and the enhanced image brightness, and may also generate noise and edge ghost. Based on histogram equalization, researchers have proposed a number of representative image enhancement algorithms, more representative contrast preserving histogram equalization BBHE, dual sub-image histogram equalization DSIHE, etc., which may lead to excessive enhancement and loss of detail.
Disclosure of Invention
The invention aims to provide an image enhancement method based on a clipping histogram, which ensures reasonable brightness contrast after image enhancement and has good detail keeping effect.
The technical solution for realizing the purpose of the invention is as follows: an image enhancement method based on clipping histograms, comprising the steps of:
step 1, calculating a median value of an image, and dividing the image into two sub-histograms according to the median value;
Step 2, calculating average brightness values of the two sub-histograms respectively;
Step 3, taking the average brightness value of the two sub-histograms as a segmentation threshold value, and dividing the original image into three sub-histograms;
Step 4, cutting each sub-histogram;
And 5, homogenizing each sub-image, and merging to obtain a complete enhanced image.
Further, the median of the image is calculated in step 1, and the image is divided into two sub-histograms according to the median, specifically as follows:
assuming a gray value range of the image [0, L-1], the probability density function h (x) of the image is defined as:
h(x)＝H(x)/N (1)
in the above formula, H (x) is the sum of pixels with gray values equal to x, and
The median of the input image is represented as a gray value M e, and the corresponding cumulative density function is 0.5;
The definition variable z is:
z(x)＝z(x-1)+h(x)，x＝0,1…L-1 (2)
and z (0) =h (0);
then M e is defined as:
Then, the histogram of the image is divided into two sub-histograms h L and h H according to M e:
hL＝h(x),x＝0,1…Me (4)
hH＝h(x),x＝Me+1，…L-1 (5)
Further, in step2, the average luminance values of the two sub-histograms are calculated respectively, which is specifically as follows:
The average luminance values of the two sub-histograms h L and h H are denoted as I ml and I mh, respectively, then
Further, in step 3, the average luminance value of the two sub-histograms is used as a segmentation threshold, and the original image is segmented into three sub-histograms, specifically:
taking I ml and I mh as segmentation thresholds, the histogram is again divided into 3 sub-histograms h 1,h2,h3, denoted as:
h1(x)＝h(x),x＝0,1…Iml (8)
h2(x)＝h(x),x＝Iml+1,…Imh (9)
h3(x)＝h(x),x＝Imh+1,…L-1(10)
Further, in step 4, clipping is performed on each sub-histogram, which is specifically as follows:
Using the average value of the frequency values of the gray values of each sub-histogram as the clipping threshold of each sub-histogram, denoted as c 1,c2,c3:
Clipping the three sub-histograms by using a clipping threshold value to obtain a clipped histogram expressed as h 1 c、h2 c、h3 c:
Normalizing the cut histogram to obtain a normalized histogram, which is expressed as:
Further, in step 5, the homogenizing treatment is performed on each sub-image, and then the merging is performed to obtain a complete enhanced image, which is specifically as follows:
the accumulated frequency CDF of the three sub-histograms is C 1,C2,C3 respectively, and the calculation formula is as follows:
After the CDF for each sub-histogram is determined, the transform function is determined using histogram equalization, resulting in an enhanced image denoted p 1、p2、p3:
p1＝Iml×C1 (23)
p2＝(Iml+1)+(Imh-Iml-1)×C2 (24)
p3＝(Imh+1)+(L-Imh-2)×C3 (25)
And combining the three enhanced sub-images to obtain a complete enhanced image.
Compared with the prior art, the invention has the remarkable advantages that: (1) The advantages of histogram segmentation and histogram clipping are fully utilized, the enhancement is controlled, the noise is suppressed, the contrast is reasonably improved, and the image has good effects on the aspects of contrast and brightness maintenance after the image is enhanced; (2) The local details of the image are well preserved, so that the image can be enhanced and naturally and unobtrusively seen.
Drawings
Fig. 1 is a flow chart of the clipping histogram-based image enhancement method of the present invention.
Fig. 2 is a graph of image enhancement contrast of the method of the present invention with other image enhancement algorithms.
Detailed description of the preferred embodiments
The invention relates to an image enhancement method based on clipping histograms, which comprises the following steps:
step 1, calculating a median value of an image, and dividing the image into two sub-histograms according to the median value;
Step 2, calculating average brightness values of the two sub-histograms respectively;
Step 3, taking the average brightness value of the two sub-histograms as a segmentation threshold value, and dividing the original image into three sub-histograms;
Step 4, cutting each sub-histogram;
And 5, homogenizing each sub-image, and merging to obtain a complete enhanced image.
Further, in a first step, the median of the image is calculated.
Assuming the gray value range of the image [0, L-1], the probability density function of the image is defined as:
h(x)＝H(x)/N (1)
in the above formula, H (x) is the sum of pixels with gray values equal to x, and
The median value of the input image is expressed as a gray value Me with an accumulated density function of 0.5.
The definition variable z is:
z(x)＝z(x-1)+h(x)，x＝0,1…L-1 (2)
and z (0) =h (0).
Then median M e is defined as:
Then, according to the median value, M e divides the histogram of the image into two sub-histograms h L and h H.
hL＝h(x),x＝0,1…Me (4)
hH＝h(x),x＝Me+1，…L-1 (5)
Further, in the second step, an average luminance value is calculated.
The average luminance values of the two sub-histograms h L and h H are denoted as I ml and I mh, respectively, then
Further, in the third step, an average luminance value is calculated.
Sub-histogram partitioning
Taking I ml and I mh as segmentation thresholds, the histogram is again divided into 3 sub-histograms h 1,h2,h3, denoted as:
h1(x)＝h(x),x＝0,1…Iml (8)
h2(x)＝h(x),x＝Iml+1,…Imh (9)
h3(x)＝h(x),x＝Imh+1,…L-1 (10)
Further, in the fourth step, the sub-histogram is cut.
The average value of the frequency values of the gray values of each sub-histogram is used as a clipping threshold of each sub-histogram, denoted as c 1,c2,c3.
Cutting the three sub-histograms by using a cutting threshold value, and obtaining a cut histogram which is expressed as:
Normalizing the cut histogram to obtain a normalized histogram, which is expressed as:
Further, in the fifth step, histogram equalization processing and merging are performed.
The accumulated frequency CDF of the three sub-histograms is C 1,C2,C3 respectively, and the calculation formula is as follows:
after the CDF for each sub-histogram is found, the transformation function is determined using histogram equalization, and the resulting enhanced image is represented as:
p1＝Iml×C1 (23)
p2＝(Iml+1)+(Imh-Iml-1)×C2 (24)
p3＝(Imh+1)+(L-Imh-2)×C3 (25)
And combining the three enhanced sub-images to obtain a complete enhanced image.
As can be seen from the simulation result of FIG. 2, the first to third rows are the first to third images in sequence, wherein the first column of images is the original image, the second column of images processed by the BBHE algorithm, the third column of images processed by the DSIHE algorithm, and the fourth column of images processed by the method of the invention. It can be seen from the figures that the first image, after enhancement, brings in significant noise with BBHE and DSIHE. The second image has a low contrast with the original image and the BBHE and DSIHE algorithms show excessive enhancement. In the third figure, the effect of BBHE enhancement is not very coordinated, DSIHE brings in significant noise. The algorithm of the invention has better effect after the three images are enhanced. The result shows that the method controls and enhances by utilizing the algorithm of the histogram clipping, suppresses noise, reasonably improves contrast, keeps the picture detail well, and enables the enhanced image to look more coordinated.

Claims (2)

1. An image enhancement method based on clipping histograms, comprising the steps of:
step 1, calculating a median value of an image, and dividing the image into two sub-histograms according to the median value;
Step 2, calculating average brightness values of the two sub-histograms respectively;
Step 3, taking the average brightness value of the two sub-histograms as a segmentation threshold value, and dividing the original image into three sub-histograms;
Step 4, cutting each sub-histogram;
Step 5, homogenizing each sub-image, and merging to obtain a complete enhanced image;
And (3) calculating the median value of the image in the step (1), and dividing the image into two sub-histograms according to the median value, wherein the method comprises the following steps of:
assuming a gray value range of the image [0, L-1], the probability density function h (x) of the image is defined as:
h(x)＝H(x)/N (1)
in the above formula, H (x) is the sum of pixels with gray values equal to x, and
The median of the input image is represented as a gray value M e, and the corresponding cumulative density function is 0.5;
The definition variable z is:
z(x)＝z(x-1)+h(x)，x＝0,1…L-1 (2)
and z (0) =h (0);
then M e is defined as:
Then, the histogram of the image is divided into two sub-histograms h L and h H according to M e:
hL＝h(x),x＝0,1…Me (4)
hH＝h(x),x＝Me+1，…L-1 (5)
And step 2, calculating average brightness values of the two sub-histograms respectively, wherein the average brightness values are as follows:
The average luminance values of the two sub-histograms h L and h H are denoted as I ml and I mh, respectively, then
And 3, taking the average brightness value of the two sub-histograms as a segmentation threshold value, and dividing the original image into three sub-histograms, wherein the method specifically comprises the following steps:
taking I ml and I mh as segmentation thresholds, the histogram is again divided into 3 sub-histograms h 1,h2,h3, denoted as:
h1(x)＝h(x),x＝0,1…Iml (8)
h2(x)＝h(x),x＝Iml+1,…Imh (9)
h3(x)＝h(x),x＝Imh+1,…L-1 (10)
and 4, clipping each sub-histogram, wherein the clipping process is specifically as follows:
Using the average value of the frequency values of the gray values of each sub-histogram as the clipping threshold of each sub-histogram, denoted as c 1,c2,c3:
Clipping the three sub-histograms by using a clipping threshold value to obtain a clipped histogram expressed as h 1 c、h2 c、h3 c:
Normalizing the cut histogram to obtain a normalized histogram, which is expressed as:
2. the image enhancement method based on clipping histograms according to claim 1, wherein in step 5, each sub-image is homogenized and combined to obtain a complete enhanced image, specifically as follows:
the accumulated frequency CDF of the three sub-histograms is C 1,C2,C3 respectively, and the calculation formula is as follows:
After the CDF for each sub-histogram is determined, the transform function is determined using histogram equalization, resulting in an enhanced image denoted p 1、p2、p3:
p1＝Iml×C1 (23)
p2＝(Iml+1)+(Imh-Iml-1)×C2 (24)
p3＝(Imh+1)+(L-Imh-2)×C3 (25)
And combining the three enhanced sub-images to obtain a complete enhanced image.
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