CN1750044A - Equalizing method for truncating histogram - Google Patents
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- CN1750044A CN1750044A CN 200510028998 CN200510028998A CN1750044A CN 1750044 A CN1750044 A CN 1750044A CN 200510028998 CN200510028998 CN 200510028998 CN 200510028998 A CN200510028998 A CN 200510028998A CN 1750044 A CN1750044 A CN 1750044A
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Abstract
The truncated histogram equalizing method includes the following steps: reading brightness values of input image; initializing truncated histogram storing array; traversing image in order to read in image pixel brightness values; judging whether truncated histogram storing array element value corresponding to some pixel brightness value is smaller than preset parameter value, if yes, adding 1 to the truncated histogram storing array element value corresponding to the pixel brightness value; if no, adding 1 to the truncated pixel sum; performing statistics on all the pixels in the image; calculating the truncated histogram CDF array; calculating the dynamic mapping list; mapping input brightness value to obtain enhanced brightness value; and reducing digital image. The truncated histogram equalizing method of the present invention is one kind of controllable equalizing technology, and can enhance effectively while inhibiting background noise separation and improving equalizing effect.
Description
Technical field
The present invention relates to a kind of equalizing method for truncating histogram, be applied to technical fields such as rest image processing, video image enhancing.
Background technology
Histogram is that output data is carried out classified statistics figure, and it has reflected the distribution probability feature of target data.Histogram equalization is that a kind of these classified statistics figure that utilizes carries out the technology that distribution characteristics is optimized, and optimizes later DATA DISTRIBUTION trend and evenly distributes.The histogram equalization technology is widely used in the figure image intensifying.
The histogram equalization technology is open by the following files:
1)Two-dimensional?Signal?and?Image?Processing，Prentice?hall，Englewoodcliffs，New?Jersey，1990
2)Digital?Image?Processing，R.C.Gonzalez，P.Wints，Addison-Wesley，Reading，Massachusetts
3)Evaluation?of?the?Effectiveness?of?Adaptive?Histogram?Equalization?forContrast?Enhancement，J.Zimmerman，S.prizer，E.Staab，E.Perry，W.McCarteney?and?B.Brenton，IEEE?Trans.on?Medical?Imaing，PP.304-312
4)Application?of?Adaptive?Histogram?Equalization?to?x-ray?chest?Image，Y.Li，W.Wang?and?D.Y.Yu，Proc.of?the?SPIE，PP.513-514，vol.2321，1994
These disclosed files studied the histogram equalization technology aspect Flame Image Process application and be practiced in medical imaging and the enhancing of radar image.
The essence of histogram equalization is that the gradient that make to strengthen the back data is directly proportional with the occurrence probability of these data, in a sub-picture, if certain is many more as numerical value, strengthen so the back it with adjacent just strong more as the contrast between the numerical value.Histogram equalization has enlarged the dynamic range of image simultaneously by expanded contrast generally speaking, make the visual experience of image improve, as Fig. 2 (a), and Fig. 2 (b) is respectively the histogram after former figure and the equilibrium, comparison diagram 2 (a), the pixel brightness of Fig. 2 (b) distributes and no longer concentrates on somewhere, thereby more even, and dynamic range is bigger.But the histogram equalization technology also has very significant disadvantages: its equilibrium result determines fully that by the distribution characteristics of data balancing procedure is uncontrolled.This shortcoming makes in the dynamic range of having expanded image simultaneously, has also strengthened noise, especially the noise of overall background.Another shortcoming of histogram equalization is the intermediate value that the mean flow rate of the image after strengthening all tends to brightness values, when the mean flow rate of former figure is worth away from this, the mean flow rate of the image after the enhancing changes too greatly, the overall visual of image also just changes bigger, does not meet application demand in many application scenarios.
Summary of the invention
A kind of equalizing method for truncating histogram provided by the invention provides a kind of controlled balancing technique, is used to improve traditional histogram equalization result.
In order to achieve the above object, the invention provides a kind of equalizing method for truncating histogram, it comprises following steps:
Step a), input digital image are if color digital image then needs by color model extraction brightness number wherein; If gray level image is then directly used half-tone information as brightness number;
Step b), initialization truncating histogram storage array CH[x]=0, x ∈ { X
i| i=0,1 ..., N-1}, wherein X
0, X
1..., X
i..., X
N-1Order is the brightness of image value of N level discretize, and X
0The minimal value of correspondence image brightness values, X
N-1The maximum value of correspondence image brightness values; Picture number and CN=0 are blocked in initialization;
Step c), traversing graph picture in order read in the brightness value of image current pixel;
Step d), whether judge the pairing truncating histogram storage of the brightness value of this pixel number component value less than predetermined parameters CountMax, if, execution in step e then), if not, execution in step f then);
Step e), the truncating histogram storage number component value of the brightness value correspondence of this pixel is added 1, again execution in step g);
Step f), the value that will block as number and CN add 1, again execution in step g);
Step g), judge whether all pixels statisticses of image to be finished, if, execution in step h then), if not, then return step c), circulation execution in step c)～step g);
Step h), calculate the CDF array of truncating histogram, be defined as CCDF[x], x ∈ { X
i| i=0,1 ..., N-1}, wherein X
0, X
1..., X
i..., X
N-1Order is the brightness of image value of N level discretize, and X
0The minimal value of correspondence image brightness values, X
N-1The maximum value of correspondence image brightness values;
CCH[x defines arrays], x ∈ { X
i| i=0,1 ..., N-1}, wherein X
0, X
1..., X
i..., X
N-1Order is the brightness of image value of N level discretize, and X
0The minimal value of correspondence image brightness values, X
N-1The maximum value of correspondence image brightness values; Calculate CCH[X
0]=CH[X
0]+CN/N;
Recycle is calculated: CCH[X
i]=CCH[X
I-1]+CH[X
i]+CN/N, i=1,2 ..., N-1;
Last cycle calculations: CCDF[X
i]=CCH[X
i]/Num, i=0,1 ..., N-1, wherein, Num is the sum of all pixels of this image;
Step I), calculate dynamic mapping table: G[X
i]=(1-K) * X
i+ K * CCDF[X
i] * N; I=0,1 ..., N-1, wherein, K is a preset parameter, and 0≤K≤1;
Step j), mapping input brightness value: the brightness value of establishing input picture is Xin, calculates the brightness value Xout of the original image after strengthening, then Xout=G[Xin];
According to the image property of input, the reduction number word image if input is coloured image, is then gone back original color image, if input is gray level image, then reduces gray level image.
In the described step c), can traverse all pixels of image in proper order by row, column, backward, fall to be listed as etc.
In the described step d), the dynamic change of CountMax can be carried out self-adaptation according to the Luminance Distribution feature, and when view data distributes when discrete, CountMax can be greatly, and view data distributes when concentrated, and CountMax can be smaller; Because CountMax must use during the statistics truncating histogram, so the self-adaptation of this frame/field CountMax should be calculated and self-adaptation according to the Luminance Distribution feature of previous frame/field; But so dynamic design CountMax:CountMax=C * Max (Sigma-C0,0), wherein, C is default constant, Sigma is the luminance standard deviation of previous frame/field, C0 is default constant, as Sigma during smaller or equal to this constant, also is that the histogram dispersion degree is when little, CountMax is zero, the truncating histogram equilibrium does not strengthen effect, and standard deviation is big more, and the histogram equalization effect is big more.Use dispersion degree can obtain better pictures and handle robustness as control device.
In the described step d), CountMax also can make following dynamic design:
CountMax=C * Max (MSE-C0,0), wherein, MSE is the brightness mean square deviation of previous frame/field.
In the described step d), CountMax also can make following dynamic design:
CountMax=C * Max (ME-C0,0), wherein, ME is that simple average is poor, and its computation process is: obtain the absolute value of the difference of the brightness of each pixel of image and mean picture brightness earlier, then to this absolute value summation of a frame/field, again should and divided by sum of all pixels, promptly obtain ME:
Wherein, Num is the pixel sum, B
jBe j pixel intensity.
In the described step d), can set mxm. CM to CountMax and limit: if the CountMax after calculating then makes CountMax=CM greater than default mxm. CM.
Described step I) in, because the noise properties of various image backgrounds and inequality, they may have different signal to noise ratio (S/N ratio)s in different brightness section.Because the luminance signal in dark more zone is weak more, then the signal to noise ratio (S/N ratio) of dark areas is lower.Be the opposing noise, equalizing coefficient K carried out the design of brightness adaptive optimization: K=min[max[g * (X
i-X
T), 0], K
Max], wherein g is a parameter preset, X
TBe threshold value, K
MaxBe upper limit control; When brightness is lower than X
TThe time, K is zero, at this moment this luminance area is unbalanced; When along with X
iIncrease, K also increases, and is excessive for preventing K, sets K
MaxLimit.
In described step a), color model can be the Y value of YUV color space, or the Y value in YCbCr space, or the V value in HSV space, or the L value in HSL space, or their equivalent expression.
Equalizing method for truncating histogram provided by the invention can be implemented effective control to image equalization by controlled variable CountMax and K.CountMax is big more, and the picture number and more little that blocks is then got over convergence tradition histogram equalization, otherwise, then trend towards keeping former histogram distribution feature more; The K value is big more, then gets over convergence tradition histogram equalization, otherwise, then trend towards keeping former histogram distribution feature more.Can select control chart as balanced intensity by control CountMax and K, change thereby control strengthens the brightness of back image.Simultaneously, the size of CountMax determined greater than CountMax can only obtain limited contrast expansion as numerical value, when can effectively restrain enhancing like this background noise is separated from background.Last this method uses truncating histogram to substitute traditional histogram in statistics and balancing procedure.
Equalizing method for truncating histogram provided by the invention is used in combination forecasting techniques between frame/field, and the truncating histogram of present frame/field is applied to the contrast expansion of next frame/field, does like this and has saved storage space.
Equalizing method for truncating histogram provided by the invention is equally applicable to analog picture signal, only need change into data image signal and get final product simulating signal is discrete in the application process of reality.
Description of drawings
Fig. 1 is the process flow diagram of equalizing method for truncating histogram provided by the invention;
Among Fig. 2, Fig. 2 (a), and Fig. 2 (b) is respectively balanced preceding figure and the histogram after the equilibrium.
Embodiment
Followingly specify a kind of preferred forms of the present invention according to Fig. 1:
As shown in Figure 1, be the process flow diagram of equalizing method for truncating histogram provided by the invention, it comprises following steps:
Step a), input digital image are if color digital image then needs by color model extraction brightness number wherein; If gray level image is then directly used half-tone information as brightness number;
Step b), initialization truncating histogram storage array CH[x]=0, x ∈ { X
i| i=0,1 ..., 255}, X
i=i; The corresponding utmost point black of i=0, the i=255 correspondence is white extremely; Picture number and CN=0 are blocked in initialization;
Step c), traversing graph picture in order read in the brightness value of image current pixel;
Step d), whether judge the pairing truncating histogram storage of the brightness value of this pixel number component value less than predetermined parameters CountMax, if, execution in step e then), if not, execution in step f then);
Step e), the truncating histogram storage number component value of the brightness value correspondence of this pixel is added 1, again execution in step g);
Step f), the value that will block as number and CN add 1, again execution in step g);
Step g), judge whether all pixels statisticses of image to be finished, if, execution in step h then), if not, then return step c), circulation execution in step c)～step g);
Step h), calculate the CDF array of truncating histogram, be defined as CCDF[x], x ∈ { X
i| i=0,1 ..., 255}, X
i=i; The corresponding utmost point black of i=0, the i=255 correspondence is white extremely.
CCH[x defines arrays], x ∈ { X
i| i=0,1 ..., 255}, X
i=i; The corresponding utmost point black of i=0, the i=255 correspondence is white extremely.Calculate CCH[X
0]=CH[X
0]+CN/256;
Recycle is calculated: CCH[X
i]=CCH[X
I-1]+CH[X
i]+CN/256, i=1,2 ..., 255;
Last cycle calculations: CCDF[X
i]=CCH[X
i]/Num, i=0,1 ..., 255, wherein, Num is the sum of all pixels of this image;
Step I), calculate dynamic mapping table: G[X
i]=(1-K) * X
i+ K * CCDF[X
i] * N; I=0,1 ..., 255, wherein, K is a preset parameter, and 0≤K≤1;
Step j), mapping input brightness value: the brightness value of establishing input picture is Xin, calculates the brightness value Xout of the original image after strengthening, then Xout=G[Xin];
According to the image property of input, the reduction number word image if input is coloured image, is then gone back original color image, if input is gray level image, then reduces gray level image.
In the described step c), can traverse all pixels of image in proper order by row, column, backward, fall to be listed as etc.
In the described step d), the dynamic change of CountMax can be carried out self-adaptation according to the Luminance Distribution feature, and when view data distributes when discrete, CountMax can be greatly, and view data distributes when concentrated, and CountMax can be smaller; Because CountMax must use during the statistics truncating histogram, so the self-adaptation of this frame/field CountMax should be calculated and self-adaptation according to the Luminance Distribution feature of previous frame/field; But so dynamic design CountMax:CountMax=C * Max (Sigma-C0,0), wherein, C is default constant, Sigma is the luminance standard deviation of previous frame/field, C0 is default constant, as Sigma during smaller or equal to this constant, also is that the histogram dispersion degree is when little, CountMax is zero, the truncating histogram equilibrium does not strengthen effect, and standard deviation is big more, and the histogram equalization effect is big more.Use dispersion degree can obtain better pictures and handle robustness as control device.
In the described step d), CountMax also can make following dynamic design:
CountMax=C * Max (MSE-C0,0), wherein, MSE is the brightness mean square deviation of previous frame/field.
In the described step d), CountMax also can make following dynamic design:
CountMax=C * Max (ME-C0,0), wherein, ME is that simple average is poor, and its computation process is: obtain the absolute value of the difference of the brightness of each pixel of image and mean picture brightness earlier, then to this absolute value summation of a frame/field, again should and divided by sum of all pixels, promptly obtain ME:
Wherein, Num is the pixel sum, B
jBe j pixel intensity.
In the described step d), can set mxm. CM to CountMax and limit: if the CountMax after calculating then makes CountMax=CM greater than default mxm. CM.
Described step I) in, because the noise properties of various image backgrounds and inequality, they may have different signal to noise ratio (S/N ratio)s in different brightness section.Because the luminance signal in dark more zone is weak more, then the signal to noise ratio (S/N ratio) of dark areas is lower.Be the opposing noise, equalizing coefficient K carried out the design of brightness adaptive optimization: K=min[max[g * (X
i-X
T), 0], K
Max], wherein g is a parameter preset, X
TBe threshold value, K
MaxBe upper limit control; When brightness is lower than X
TThe time, K is zero, at this moment this luminance area is unbalanced; When along with X
iIncrease, K also increases, and is excessive for preventing K, sets K
MaxLimit.
In described step a), color model can be the Y value of YUV color space, or the Y value in YCbCr space, or the V value in HSV space, or the L value in HSL space, or their equivalent expression.
Equalizing method for truncating histogram provided by the invention can be implemented effective control to image equalization by controlled variable CountMax and K.CountMax is big more, and the picture number and more little that blocks is then got over convergence tradition histogram equalization, otherwise, then trend towards keeping former histogram distribution feature more; The K value is big more, then gets over convergence tradition histogram equalization, otherwise, then trend towards keeping former histogram distribution feature more.Can select control chart as balanced intensity by control CountMax and K, change thereby control strengthens the brightness of back image.Simultaneously, the size of CountMax determined greater than CountMax can only obtain limited contrast expansion as numerical value, when can effectively restrain enhancing like this background noise is separated from background.Last this method uses truncating histogram to substitute traditional histogram in statistics and balancing procedure.
Equalizing method for truncating histogram provided by the invention is used in combination forecasting techniques between frame/field, and the truncating histogram of present frame/field is applied to the contrast expansion of next frame/field, does like this and has saved storage space.
Equalizing method for truncating histogram provided by the invention is equally applicable to analog picture signal, only need change into data image signal and get final product simulating signal is discrete in the application process of reality.
Claims (10)
1. an equalizing method for truncating histogram is characterized in that, comprises following steps:
Step a), input digital image are if color digital image then needs by color model extraction brightness number wherein; If gray level image is then directly used half-tone information as brightness number;
Step b), initialization truncating histogram storage array CH[x]=0, x ∈ { X
i| i=0,1 ..., N-1}, wherein X
0, X
1..., X
i..., X
N-1Order is the brightness of image value of N level discretize, and X
0The minimal value of correspondence image brightness values, X
N-1The maximum value of correspondence image brightness values; Picture number and CN=0 are blocked in initialization;
Step c), traversing graph picture in order read in the brightness value of image current pixel;
Step d), whether judge the pairing truncating histogram storage of the brightness value of this pixel number component value less than predetermined parameters CountMax, if, execution in step e then), if not, execution in step f then);
Step e), the truncating histogram storage number component value of the brightness value correspondence of this pixel is added 1, again execution in step g);
Step f), the value that will block as number and CN add 1, at execution in step g);
Step g), judge whether all pixels statisticses of image to be finished, if, execution in step h then), if not, then return step c), circulation execution in step c)～step g);
Step h), calculate the CDF array of truncating histogram, be defined as CCDF[x], x ∈ { X
i| i=0,1 ..., N-1}, wherein X
0, X
1..., X
i..., X
N-1Order is the brightness of image value of N level discretize, and X
0The minimal value of correspondence image brightness values, X
N-1The maximum value of correspondence image brightness values;
CCH[x defines arrays], x ∈ { X
i| i=0,1 ..., N-1}, wherein X
0, X
1..., X
i..., X
N-1Order is the brightness of image value of N level discretize, and X
0The minimal value of correspondence image brightness values, X
N-1The maximum value of correspondence image brightness values;
Calculate CCH[X
0]=CH[X
0]+CN/N;
Recycle is calculated: CCH[X
i]=CCH[X
I-1]+CH[X
i]+CN/N, i=1,2 ..., N-1;
Last cycle calculations: CCDF[X
i]=CCH[X
i]/Num, i=0,1 ..., N-1, wherein, Num is the sum of all pixels of this image;
Step I), calculate dynamic mapping table: G[X
i]=(1-K) * X
i+ K * CCDF[X
i] * N; I=0,1 ..., N-1, wherein, K is a preset parameter, and 0≤K≤1;
Step j), mapping input brightness value: the brightness value of establishing input picture is Xin, calculates the brightness value Xout of the original image after strengthening, then Xout=G[Xin];
According to the image property of input, the reduction number word image if input is coloured image, is then gone back original color image, if input is gray level image, then reduces gray level image.
2. equalizing method for truncating histogram as claimed in claim 1 is characterized in that, in the described step c), can traverse all pixels of image by row, column, backward, fall to be listed as etc. in proper order.
3. equalizing method for truncating histogram as claimed in claim 1 is characterized in that, can carry out dynamic design to CountMax: CountMax=C * Max (Sigma-C0,0), wherein, C is default constant, Sigma is the luminance standard deviation of previous frame/field, and C0 is default constant.
4. equalizing method for truncating histogram as claimed in claim 3 is characterized in that, can carry out dynamic design: CountMax=C * Max (MSE-C0,0) to CountMax, and wherein, MSE is the brightness mean square deviation of previous frame/field.
5. equalizing method for truncating histogram as claimed in claim 4 is characterized in that, can carry out dynamic design: CountMax=C * Max (ME-C0,0) to CountMax, and wherein, ME is that simple average is poor.
6. equalizing method for truncating histogram as claimed in claim 5, it is characterized in that, the computation process of described simple average difference ME: the absolute value of obtaining the difference of the brightness of each pixel of image and mean picture brightness earlier, then this absolute value of a frame/field is sued for peace, again should and divided by sum of all pixels, that is:
Wherein, Num is the pixel sum, B
jBe j pixel intensity.
7. equalizing method for truncating histogram as claimed in claim 6 is characterized in that, in the described step d), sets the mxm. CM of CountMax: if the value of the CountMax that calculates then makes CountMax=CM greater than default mxm. CM.
8. equalizing method for truncating histogram as claimed in claim 1 is characterized in that step I) in, equalizing coefficient K is carried out the design of brightness adaptive optimization: K=min[max[g * (X
i-X
T), 0], K
Max], wherein g is a parameter preset, X
TBe predetermined threshold value parameter, K
MaxBe the preset upper limit controlled variable.
9. equalizing method for truncating histogram as claimed in claim 1, it is characterized in that, in described step a), color model can be the Y value of YUV color space, or the Y value in YCbCr space, or the V value in HSV space, or the L value in HSL space, or their equivalent expression.
10. equalizing method for truncating histogram as claimed in claim 1 is characterized in that, is used in combination forecasting techniques between frame/field, and the truncating histogram of present frame/field is applied to the contrast expansion of next frame/field, saves storage space.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100507944C (en) * | 2006-11-27 | 2009-07-01 | 北京中星微电子有限公司 | Image format conversion method, image processing method and system |
| CN102496152A (en) * | 2011-12-01 | 2012-06-13 | 四川虹微技术有限公司 | Self-adaptive image contrast enhancement method based on histograms |
| CN105335946A (en) * | 2015-09-22 | 2016-02-17 | 中国科学院西安光学精密机械研究所 | Histogram compactness transformation method |
| CN107481206A (en) * | 2017-08-28 | 2017-12-15 | 湖南友哲科技有限公司 | MIcrosope image background equalization Processing Algorithm |
| US20190139204A1 (en) * | 2016-09-12 | 2019-05-09 | Boe Technology Group Co., Ltd. | Method and device for adjusting grayscale values of image |
| CN109801246A (en) * | 2019-01-10 | 2019-05-24 | 华侨大学 | A kind of color histogram equalization methods of adaptive threshold |
| CN110996174A (en) * | 2019-12-19 | 2020-04-10 | 深圳市迅雷网络技术有限公司 | Video image quality enhancement method and related equipment thereof |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR0176601B1 (en) * | 1996-05-21 | 1999-05-01 | 김광호 | Picture quality improving method & circuit using low-filtering and histogram equalization |
| KR100213109B1 (en) * | 1996-06-20 | 1999-08-02 | 윤종용 | Circuit for improving picture quality by using noise reduction and histogram equalization and method thereof |
| KR100189922B1 (en) * | 1996-06-20 | 1999-06-01 | 윤종용 | Circuit and method for improving contrast by using histogram equalization |
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2005
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| CN100507944C (en) * | 2006-11-27 | 2009-07-01 | 北京中星微电子有限公司 | Image format conversion method, image processing method and system |
| CN102496152A (en) * | 2011-12-01 | 2012-06-13 | 四川虹微技术有限公司 | Self-adaptive image contrast enhancement method based on histograms |
| CN102496152B (en) * | 2011-12-01 | 2014-08-27 | 四川虹微技术有限公司 | Self-adaptive image contrast enhancement method based on histograms |
| CN105335946A (en) * | 2015-09-22 | 2016-02-17 | 中国科学院西安光学精密机械研究所 | Histogram compactness transformation method |
| CN105335946B (en) * | 2015-09-22 | 2018-01-19 | 中国科学院西安光学精密机械研究所 | A kind of histogram compactness transform method |
| US20190139204A1 (en) * | 2016-09-12 | 2019-05-09 | Boe Technology Group Co., Ltd. | Method and device for adjusting grayscale values of image |
| US10467737B2 (en) * | 2016-09-12 | 2019-11-05 | Boe Technology Group Co., Ltd. | Method and device for adjusting grayscale values of image |
| CN107481206A (en) * | 2017-08-28 | 2017-12-15 | 湖南友哲科技有限公司 | MIcrosope image background equalization Processing Algorithm |
| CN109801246A (en) * | 2019-01-10 | 2019-05-24 | 华侨大学 | A kind of color histogram equalization methods of adaptive threshold |
| CN109801246B (en) * | 2019-01-10 | 2022-11-01 | 华侨大学 | Global histogram equalization method for adaptive threshold |
| CN110996174A (en) * | 2019-12-19 | 2020-04-10 | 深圳市迅雷网络技术有限公司 | Video image quality enhancement method and related equipment thereof |
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