CN103700066B - Method for processing video image of portable night vision instrument - Google Patents
Method for processing video image of portable night vision instrument Download PDFInfo
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- CN103700066B CN103700066B CN201310641794.5A CN201310641794A CN103700066B CN 103700066 B CN103700066 B CN 103700066B CN 201310641794 A CN201310641794 A CN 201310641794A CN 103700066 B CN103700066 B CN 103700066B
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- 230000004297 night vision Effects 0.000 title claims abstract description 18
- 238000012545 processing Methods 0.000 title claims abstract description 9
- 238000013507 mapping Methods 0.000 claims abstract description 16
- 230000002708 enhancing effect Effects 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 6
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Abstract
The invention discloses a method for processing video images of a portable night vision instrument. The images acquired by the portable night vision instrument are processed, a video enhancement algorithm based on a Retinex algorithm and a dynamic histogram mapping algorithm are combined, the Retinex algorithm is adopted for the partial enhancement of the image, at the same time, the dynamic histogram mapping algorithm is adopted for overall image enhancement, and finally, two enhancement results are combined in a weight mode according to image analyzing results, and a final enhancement image is obtained. By using the method, the enhancement, the noise reduction, the color sharpening and the like of the night vision image are processed, and image contrast and image definition are increased to obtain the image approximate to an original image and is in accord with the visual characteristic of eyes.
Description
Technical field
The invention belongs to image processing method technical field, especially relate to a kind of image procossing of portable night vision equipment
Method.
Background technology
Portable night vision equipment has been widely used for night investigation, explores and outdoor activities.Active by technical limitations
Night vision image has the features such as local contrast is high, and Luminance Distribution is concentrated, and passive type night vision image is then image blurring, target detail
It is difficult to differentiate.
The image procossing of existing night vision device typically adopts simple denoising and contrast enhancing etc. to process, and these process are deposited
Disposal ability limited, cannot expanded images dynamic range the shortcomings of.
Content of the invention
It is an object of the invention to the deficiency improving prior art provide a kind of night vision image is carried out strengthen, de-noising, color
Color sharpen etc. process, improve picture contrast, definition, with obtain with original image close to and meet the figure of human-eye visual characteristic
The portable night vision equipment method of video image processing of picture.
The object of the present invention is achieved like this, and a kind of portable night vision equips method of video image processing, is will be portable
The image of formula night-vision equipment collection is processed, and is characterized in that the method comprises the following steps:
A, first the every two field picture in video is individually analyzed, image is smoothed, to remove making an uproar in image
Sound, obtains rectangular histogram h of image and normalization variance c of image；
B, the multiple dimensioned retinex algorithm to image application enhancements are processed, and are to be obtained using the template of three yardsticks
Enhanced image i1, i2, i3 under different scale, further according to the value of normalization variance c of image, select different weight coefficients
Enhancing image under different scale is merged by w1, w2, w3 according to weight, obtains final enhancing image i, and i=w1*i1+
W2*i2+w3*i3, the selection mode of weight is as follows:
C >=0.7 when, w1=0.5, w2=0.3, w3=0.2
0.3 < c < when 0.7, w1=0.3, w2=0.4, w3=0.3
C < when 0.3, w1=0.2, w2=0.3, w3=0.5
Wherein w1/i1, w2/i2, w3/i3 represent the weight of little yardstick, mesoscale and large scale and enhanced figure respectively
Picture, divides equally between each yardstick；
C, the result according to graphical analyses, apply dynamic histogram mapping algorithm,
Choose a, abscissa xa, xb of 2 points of b, 2 points of a, b are by the corresponding gray level of energy value at rectangular histogram two ends each 5%
Do not determine, the ordinate value that 2 points of a, b is calculated as follows:
ya=xa*(1-β)
yb=xb*(1+β)
Wherein β is partial to the tolerance of 0 or 255 degree for rectangular histogram, if h1 is the summation of 0-127 magnitude in image histogram,
H2 is the summation of 128-255 magnitude in rectangular histogram, then:
β=|h1-h2|/h
It can be seen that, if the more equilibrium of image distribution, h1 is about close with h2, and β will convergence be 0, the conversion that image is done
Amplitude is just less；If gray scale skewness weighing apparatus in contrary image, algorithm will be according to histogram distribution dynamic mapping pixel value；
D, the result of retinex algorithm and dynamic histogram mapping algorithm is combined in the way of respectively accounting for 50% weight,
Obtain final image.
Compared with the prior art the present invention has following distinguishing feature and good effect: processing method of the present invention combines base
In video enhancement algorithm and two kinds of image processing methods of dynamic histogram mapping algorithm of retinex, after improving
Retinex algorithm strengthens to image, carries out the image enhaucament of the overall situation using the algorithm of dynamic histogram mapping, finally simultaneously
Two kinds of enhanced results are combined in the form of weight, obtains final enhancing image；By the real-time analysis to video
The weight of adaptive adjustment algorithm and parameter are so that it is adapted to various weather and light conditions.After improving
Being implemented in combination with image enhaucament, de-noising, color sharpening etc. process of retinex algorithm and dynamic histogram mapping algorithm；The present invention
Every two field picture in video is individually analyzed, first image is smoothed, to remove the noise in image, due to video
The content change of image is larger, and single algorithm parameter cannot be applied in different video scenes, to actual Video processing
For algorithm, it is necessarily required to using adaptive image processing method, and these methods obtain image firstly the need of analysis of the image
Statistical information, as the foundation of setup parameter；The three-channel weight of traditional multiple dimensioned retinex algorithm is identical, right
It is impossible to obtain best reinforced effects for the different image of details, the present invention proposes to enrich degree according to details in image
Give different scale strengthens image with the thought of different weights, has well adapted to different images content and the enhancing of scene will
Ask；The present invention, according to the result of graphical analyses, applies dynamic histogram mapping algorithm, if the more equilibrium of image distribution, image
The conversion amplitude done is just less, if gray scale skewness weighing apparatus in contrary image, algorithm dynamically will reflect according to histogram distribution
Penetrate pixel value；Due to the complexity of Video processing, single algorithm is difficult to deal with all of scene, or all takes in any scene
Optimum effect, the result that this is accomplished by obtaining many algorithms integrates, and this algorithm is by retinex algorithm and dynamically straight
The result of square figure mapping algorithm is combined with certain weight proportion, obtains final image.Weight relationship may be configured as through
Test value, typically take (0.5,0.5), that is, two kinds of algorithms respectively account for the proportion of half.
Specific embodiment
Embodiment, a kind of portable night vision equips method of video image processing, is by the figure of portable night vision equipment collection
As being processed, the method is individually analyzed to the every two field picture in video first, image is smoothed, to remove image
In noise, obtain rectangular histogram h of image and normalization variance c of image；The multiple dimensioned retinex of image application enhancements is calculated
Method is processed, and is to obtain enhanced image i1, i2, i3 under different scale using the template of three yardsticks, further according to image
The value of normalization variance c, selects different weight coefficient w1, w2, w3, is carried out the enhancing image under different scale according to weight
Merge, obtain final enhancing image i, i=w1*i1+w2*i2+w3*i3, the selection mode of weight is as follows:
C >=0.7 when, w1=0.5, w2=0.3, w3=0.2
0.3 < c < when 0.7, w1=0.3, w2=0.4, w3=0.3
C < when 0.3, w1=0.2, w2=0.3, w3=0.5
Wherein w1/i1, w2/i2, w3/i3 represent the weight of little yardstick, mesoscale and large scale and enhanced figure respectively
Picture, divides equally between each yardstick；According to the result of graphical analyses, apply dynamic histogram mapping algorithm, choose a, 2 points of b,
Abscissa xa, xb of 2 points of a, b is determined by the corresponding grey level of energy value at rectangular histogram two ends each 5%, the vertical seat of 2 points of a, b
Scale value is calculated as follows:
ya=xa*(1-β)
yb=xb*(1+β)
Wherein β is partial to the tolerance of 0 or 255 degree for rectangular histogram, if h1 is the summation of 0-127 magnitude in image histogram,
H2 is the summation of 128-255 magnitude in rectangular histogram, then:
β=|h1-h2|/h
It can be seen that, if the more equilibrium of image distribution, h1 is about close with h2, and β will convergence be 0, the conversion that image is done
Amplitude is just less；If gray scale skewness weighing apparatus in contrary image, algorithm will be according to histogram distribution dynamic mapping pixel value；Will
The result of retinex algorithm and dynamic histogram mapping algorithm is combined in the way of respectively accounting for 50% weight, obtains final figure
Picture.
Claims (1)
1. a kind of portable night vision equips method of video image processing, is by the image of portable night vision equipment collection
Reason, is characterized in that the method comprises the following steps:
A, first the every two field picture in video is individually analyzed, image is smoothed, to remove the noise in image, obtains
To the rectangular histogram of image and normalization variance c of image；
B, the multiple dimensioned retinex algorithm to image application enhancements are processed, and are to obtain difference using the template of three yardsticks
Enhanced image i1, i2, i3 under yardstick, further according to the value of normalization variance c of image, select different weight coefficient w1, w2,
Enhancing image under different scale is merged by w3 according to weight, obtains final enhancing image i, and i=w1 × i1+w2 ×
I2+w3 × i3, the selection mode of weight is as follows:
C >=0.7 when, w1=0.5, w2=0.3, w3=0.2
0.3 < c < when 0.7, w1=0.3, w2=0.4, w3=0.3
During c≤0.3, w1=0.2, w2=0.3, w3=0.5
Wherein w1, w2, w3 represent the weight of little yardstick, mesoscale and large scale respectively；I1, i2, i3 represent respectively little yardstick, in
Enhanced image under the weight of yardstick and large scale；
C, the result according to graphical analyses, apply dynamic histogram mapping algorithm,
Choose a, abscissa xa, xb of 2 points of b, 2 points of a, b are true by the corresponding grey level of energy value at rectangular histogram two ends each 5%
Fixed, the ordinate value that 2 points of a, b is calculated as follows:
Ya=xa × (1- β),
Yb=xb × (1+ β),
Wherein β is partial to the tolerance of 0 or 255 degree for rectangular histogram, if h1 is the summation of 0-127 magnitude in image histogram, h2 is
The summation of 128-255 magnitude in rectangular histogram, then:
β=| h1-h2 |/h
It can be seen that, if the more equilibrium of image distribution, h1 and h2 is closer to β will convergence be 0, the conversion amplitude that image is done
Just less；If gray scale skewness weighing apparatus in contrary image, according to histogram distribution dynamic mapping pixel value；
D, by the result of improved multiple dimensioned retinex algorithm and dynamic histogram mapping algorithm in the way of respectively accounting for 50% weight
Combine, obtain final image.
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| CN108198138B (en) * | 2017-11-24 | 2019-05-03 | 北京邮电大学 | A kind of night effect minimizing technology and device for monitor video |
| CN112396009A (en) * | 2020-11-24 | 2021-02-23 | 广东国粒教育技术有限公司 | Calculation question correcting method and device based on full convolution neural network model |
| CN114757854B (en) * | 2022-06-15 | 2022-09-02 | 深圳市安星数字***有限公司 | Night vision image quality improving method, device and equipment based on multispectral analysis |
Citations (2)
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| US6788822B1 (en) * | 1999-08-31 | 2004-09-07 | Sharp Kabushiki Kaisha | Method and device for correcting lightness of image |
| CN103413275A (en) * | 2013-07-26 | 2013-11-27 | 北京工业大学 | Retinex night image enhancement method based on gradient zero norm minimum |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6788822B1 (en) * | 1999-08-31 | 2004-09-07 | Sharp Kabushiki Kaisha | Method and device for correcting lightness of image |
| CN103413275A (en) * | 2013-07-26 | 2013-11-27 | 北京工业大学 | Retinex night image enhancement method based on gradient zero norm minimum |
Non-Patent Citations (3)
| Title |
|---|
| An Enhancement Algorithm for Video Surveillance Image Based on Multi-scale Retinex;Jiang tao等;《2011International Conference on Computational and Information Sciences》;20111231;第22-25页 * |
| 改进的多尺度Retinex图像增强算法;王小明等;《计算机应用》;20100831;第30卷(第8期);第2091-2093页 * |
| 自适应图像直方图均衡算法;张懿等;《浙江大学学报（工学版）》;20070430;第41卷(第4期);第630-633页 * |
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Inventor after: Song Youchun Inventor after: Song Yongsheng Inventor after: Qi Chen Inventor before: Song Youchun Inventor before: Song Yongsheng Inventor before: Zhang Yinsheng Inventor before: Lin Chunliang Inventor before: Guo Zhongyue Inventor before: Zhao Haozhi |
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