CN104680500A - Image intensification algorithm based on histogram equalization - Google Patents
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- CN104680500A CN104680500A CN201510067804.8A CN201510067804A CN104680500A CN 104680500 A CN104680500 A CN 104680500A CN 201510067804 A CN201510067804 A CN 201510067804A CN 104680500 A CN104680500 A CN 104680500A
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Abstract
The invention provides an image intensification algorithm based on histogram equalization. The histogram equalization algorithm is one of the most common and most important algorithms applied to image intensification spatial domain methods. According to the algorithm, based on probability theory, gray distribution of the images are adjusted through grey transformation, thus the enhancement purpose is achieved by improving the image contrast. As the algorithm is simple in calculation and large in included information, the image intensification algorithm is widely applied to image intensification treatment. For most applications with low detailed requirements on image intensification, the excellent enhancement effect is realized.
Description
Technical field
The present invention relates to a kind of algorithm for image enhancement, particularly relate to a kind of algorithm for image enhancement based on histogram equalization.
Background technology
Histogram equalization is one of method common in image enhancement processing, and its basic thought is by the distribution of equalization processing adjustment gradation of image, reaches the object improving picture contrast.Because picture contrast is the key factor determining piece image subjective quality, therefore histogram equalization is widely used in the enhancing process of image.Image histogram describes the gray level content of image, and including very abundant information, is a kind of very important analysis tool in image procossing.Mathematically, image histogram is the function of each gray-scale value statistical property of image and image intensity value, gives number of times or probability that in image, each gray level occurs; From figure, it is an X-Y scheme, and horizontal ordinate represents the gray level of each pixel in image, and ordinate represents number of times or the probability of the appearance of each each pixel of gray level epigraph.
Summary of the invention
The present invention mainly provides a kind of algorithm for image enhancement based on histogram equalization, by the distribution of equalization processing adjustment gradation of image, reaches and improves picture contrast.
In order to realize object of the present invention, the invention provides a kind of algorithm for image enhancement based on histogram equalization, it is characterized in that, the step of described algorithm is:
(1) make variable r and s difference representative image strengthen the pixel grey scale grade of front and back, corresponding grey level distribution probability density is respectively P (r)
rwith P (s)
s, by image gray levels r and s normalization between [0,1];
(2) each gray-level pixels number n of original image is added up
k, k=0,1...L-1;
(3) histogram of original graph is calculated, i.e. the probability density P (r) of each gray level
r=n
k/ n;
(4) cumulative distribution function S is calculated
k∑ P (r)
r, k=0,1,2...L-1;
(5) cumulative distribution function S is calculated
k=int [(L-1) S
k+ 0.5], k=0,1...L-1
(6) k is utilized
rand k
smapping relations, amendment original image gray level, obtain strengthen image, make image histogram be approaches uniformity distribution.
Preferably, it is characterized in that, in step (1), r=0 represents black, and r=1 represents white.
Preferably, it is characterized in that, k in step (5)
rand k
smapping relations be a greyscale transformation function T, make convert gray-scale value s=T (r).
Preferably, it is characterized in that function T must meet 2 conditions:
(1) T (r) is a monodrome monotone increasing function in the scope of 0≤r≤1;
(2) to 0≤r≤1,0≤T (r)≤1 must be had;
Preferably, ensure that T (r) inverse transformation exists, and each gray level of original image still keeps the ordering from black to white after the conversion.
Preferably, it is characterized in that, ensure the consistance of grey scale pixel value dynamic range before and after T (r) conversion, before and after image conversion, have same grey level range.
Beneficial effect: the invention provides a kind of algorithm for image enhancement based on histogram equalization, algorithm of histogram equalization is one of the most frequently used, most important algorithm in image enhaucament spatial domain method, this algorithm is based on probability theory, use ash conversion to realize adjusting the intensity profile of image, thus improve the object that picture contrast reaches enhancing.Because it calculates simple, comprise and contain much information, be widely used among image enhancement processing.Requiring it is not very high most of application scenarios to enhancing image detail, good enhancing effect can be played.
Embodiment
Below in conjunction with embodiment, the present invention is described in further details.
The invention provides a kind of algorithm for image enhancement based on histogram equalization, the steps include:
(1) make variable r and s difference representative image strengthen the pixel grey scale grade of front and back, corresponding grey level distribution probability density is respectively P (r)
rwith P (s)
s, by image gray levels r and s normalization between [0,1];
(2) each gray-level pixels number n of original image is added up
k, k=0,1...L-1;
(3) histogram of original graph is calculated, i.e. the probability density P (r) of each gray level
r=n
k/ n;
(4) cumulative distribution function S is calculated
k∑ P (r)
r, k=0,1,2...L-1;
(5) cumulative distribution function S is calculated
k=int [(L-1) S
k+ 0.5], k=0,1...L-1
(6) k is utilized
rand k
smapping relations, amendment original image gray level, obtain strengthen image, make image histogram be approaches uniformity distribution.
Wherein, in step (1), r=0 represents black, and r=1 represents white, it is characterized in that, k in step (5)
rand k
smapping relations be a greyscale transformation function T, make convert gray-scale value s=T (r), it is characterized in that function T must meet 2 conditions:
(1) T (r) is a monodrome monotone increasing function in the scope of 0≤r≤1;
(2) to 0≤r≤1,0≤T (r)≤1 must be had;
Ensure that T (r) inverse transformation exists simultaneously, and each gray level of original image still keeps the ordering from black to white after the conversion, ensure the consistance of grey scale pixel value dynamic range before and after T (r) conversion, before and after image conversion, have same grey level range.
With 64 × 64 pixels, the picture of 8 gray shade scales is example, and it is as shown in the table for the probability distribution of its each gray level:
Adopt above-mentioned algorithm by its histogram equalization, concrete computation process can be divided into following three steps to carry out:
(1) according to the data of upper table by its histogram, s
0=T (r
0)=∑ P (r
0)=0.19, the like s
1, s
2, s
3, s
4, s
5, s
6, s
7;
(2) formula S is utilized
k=int [(L-1) S
k+ 0.5], k=0,1...L-1 can obtain S
0=1, S
1=3, S
2=5, S
3=6, S
4=6, S
5=7, S
6=7, S
7=7;
(3) then identical value merger is got up, the revised gray scale transformation function of histogram equalization can be obtained, be followed successively by: S
0=1, S
1=3, S
2=5, S
3=6, S
4=7;
(4) pixel count of corresponding former gray level is added the pixel count that can obtain new gray level, add up each gray-level pixels of new histogram: n0=790, n1=1023, n2=850, n3=985, n4=448, new grey level distribution is: P (S
0)=790/4096=0.19, P (S
1)=1023/4096=0.25, P (S
2)=850/4096=0.21P (S
3)=985/4096=0.24P (S
4)=448/4096=0.11.
Compared with former histogram, it has seemed evenly, but and non-fully is even.This is due in equalization process, gray level fewer for pixel several on former histogram be integrated in a new gray level, and the grey level interval causing pixel more is by the cause widened.That is histogram equalization be reduce image gray level for cost is to exchange the expansion of contrast for.Due to can not by each pixel transform of same gray-scale value to different gray levels, therefore histogram equalization can only be approximate equalization histogram, the horizontal linear equilibrium result that can not realize ideal.
The invention provides a kind of algorithm for image enhancement based on histogram equalization, algorithm of histogram equalization is one of the most frequently used, most important algorithm in image enhaucament spatial domain method, this algorithm is based on probability theory, use ash conversion to realize adjusting the intensity profile of image, thus improve the object that picture contrast reaches enhancing.Because it calculates simple, comprise and contain much information, be widely used among image enhancement processing.Requiring it is not very high most of application scenarios to enhancing image detail, good enhancing effect can be played.
As known by the technical knowledge, the present invention can be realized by other the embodiment not departing from its Spirit Essence or essential feature.Therefore, above-mentioned disclosed embodiment, with regard to each side, all just illustrates, is not only.Within the scope of the present invention all or be all included in the invention being equal to the change in scope of the present invention.
Claims (6)
1. based on an algorithm for image enhancement for histogram equalization, it is characterized in that, the step of described algorithm is:
(1) make variable r and s difference representative image strengthen the pixel grey scale grade of front and back, corresponding grey level distribution probability density is respectively P (r)
rwith P (s)
s, by image gray levels r and s normalization between [0,1];
(2) each gray-level pixels number n of original image is added up
k, k=0,1...L-1;
(3) histogram of original graph is calculated, i.e. the probability density P (r) of each gray level
r=n
k/ n;
(4) cumulative distribution function S is calculated
k∑ P (r)
r, k=0,1,2...L-1;
(5) cumulative distribution function S is calculated
k=int [(L-1) S
k+ 0.5], k=0,1...L-1
(6) k is utilized
rand k
smapping relations, amendment original image gray level, obtain strengthen image, make image histogram be approaches uniformity distribution.
2. the algorithm for image enhancement based on histogram equalization according to claim 1, is characterized in that, in step (1), r=0 represents black, and r=1 represents white.
3. the algorithm for image enhancement based on histogram equalization according to claim 1, is characterized in that, k in step (5)
rand k
smapping relations be a greyscale transformation function T, make convert gray-scale value s=T(r).
4. the algorithm for image enhancement based on histogram equalization according to claim 1, is characterized in that function T must meet 2 conditions:
(1) T(r) be a monodrome monotone increasing function in the scope of 0≤r≤1;
(2) to 0≤r≤1,0≤T(r must be had)≤1.
5. the algorithm for image enhancement based on histogram equalization according to claim 1, is characterized in that, ensures T(r) inverse transformation exists, and each gray level of original image still keeps the ordering from black to white after the conversion.
6. the algorithm for image enhancement based on histogram equalization according to claim 1, is characterized in that, ensures T(r) consistance of grey scale pixel value dynamic range before and after conversion, there is same grey level range before and after image conversion.
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| CN105139366A (en) * | 2015-09-23 | 2015-12-09 | 成都融创智谷科技有限公司 | Image enhancement method based on space domain |
| CN105225281A (en) * | 2015-08-27 | 2016-01-06 | 广西交通科学研究院 | A kind of vehicle checking method |
| CN105427256A (en) * | 2015-11-18 | 2016-03-23 | 浙江大华技术股份有限公司 | Infrared image enhancement method and device |
| CN105828057A (en) * | 2016-03-23 | 2016-08-03 | 武汉鸿瑞达信息技术有限公司 | High data rate (hdr) adaptive color mapping method for optimizing image recognition degree |
| CN105894474A (en) * | 2016-04-20 | 2016-08-24 | 华北电力大学（保定） | Non-linear image enhancement method, and edge detection method using the same |
| CN106651818A (en) * | 2016-11-07 | 2017-05-10 | 湖南源信光电科技有限公司 | Improved Histogram equalization low-illumination image enhancement algorithm |
| CN107730476A (en) * | 2017-09-27 | 2018-02-23 | 温州大学 | A kind of image enchancing method and device based on compressed sensing |
| CN108765443A (en) * | 2018-05-22 | 2018-11-06 | 杭州电子科技大学 | A kind of mark enhancing processing method of adaptive color Threshold segmentation |
| CN109685142A (en) * | 2018-12-25 | 2019-04-26 | 国信优易数据有限公司 | A kind of image matching method and device |
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| CN111563863A (en) * | 2020-04-22 | 2020-08-21 | 江苏理工学院 | Histogram-limited image enhancement method and device |
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| CN112926619A (en) * | 2021-01-08 | 2021-06-08 | 浙江大学 | High-precision underwater laser target recognition system |
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| CN105070061A (en) * | 2015-08-19 | 2015-11-18 | 成都正广科技有限公司 | Evidence-obtaining inspection method and system for vehicle peccancy |
| CN105070061B (en) * | 2015-08-19 | 2017-09-29 | 王恩琦 | Vehicle peccancy evidence obtaining checking method and its system |
| CN105225281A (en) * | 2015-08-27 | 2016-01-06 | 广西交通科学研究院 | A kind of vehicle checking method |
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