CN102208101A - Self-adaptive linearity transformation enhancing method of infrared image - Google Patents
Self-adaptive linearity transformation enhancing method of infrared image Download PDFInfo
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Abstract
The invention relates to a self-adaptive linearity transformation enhancing method of an infrared image and is directed to the field of image enhancement technology. According to a histogram statistics of infrared image tonal values, a weight average value of all pixel tonal values av is calculated. A calculated gray scale sampling range [a, b] is combined to obtain the three points (a, 0), (av, 127) and (b, 255) in a rectangular coordinate system corresponding to the histogram. The tonal values are segmented and selected based on taking segments by the three points. A gray scale transformation is carried out towards the present image to realize enhancing the image. According to the invention, information on image scene and gray scale distribution is fully used, the gray scale distribution of a target scene is maximally used, and the disturbance from the background noise is maximally discarded. The invention not only suits for image enhancement correction of a specific object scene, but also suits for real-time self-adaptive image enhancement of same stage scenes, thereby substantially improving the usage range and enhancement effect of the linearity transformation method.
Description
Technical field
The invention belongs to the image enhancement technique field, be specifically related to a kind of adaptive line conversion Enhancement Method of infrared image.
Background technology
In infrared image strengthens, big city uses the greyscale transformation enhancement algorithms, such algorithm belongs to the spatial domain Enhancement Method of image, such conversion all is a kind of conversion with the gray-scale value r value of being mapped to s of pixel, wherein linear transformation is according to exponential function the pixel value of image to be changed, usually the gray scale sample range is chosen a fixed value in traditional method, or the manual size of indexing value, but can not be according to the change parametric variable of image adaptive in processing procedure.The shortcoming of this method is: for the infrared image under the dissimilar or different scenes can't be real-time the renewal correction parameter, cause image bright or dark excessively sight can occur.
Summary of the invention
The adaptive line conversion Enhancement Method that the purpose of this invention is to provide a kind of infrared image can't real-time update be proofreaied and correct the infrared image parameter to solve, and causes image bright or dark excessively problem to occur.
The adaptive line conversion Enhancement Method of a kind of infrared image of the present invention calculates the wherein weighted mean value av of all grey scale pixel values according to the statistics with histogram of an infrared image gray-scale value, in conjunction with the gray scale sample range [a that calculates, b], obtain corresponding to the point of three in the rectangular coordinate system on the histogram (a, 0), (av, 127), (b, 255) are got segmentation according to these 3 and gray-scale value is cut apart are chosen, current picture carries out greyscale transformation, to realize the enhancing to image.
Further, a described infrared image is the k two field picture, is of a size of Lx(k) * Ly(k), Lx (k), Ly (k) are respectively the length and the width of k two field picture, add up the grey level histogram of all pixels of k two field picture, and formula is
, j=0,1,2 ..., L-1; In the formula, have the sum of pixel in the n presentation video,
In the presentation video
The number of times that individual gray level occurs, L represents that the total number of gray level is Lx(k) * Ly(k),
Expression the
The probability of level gray level, i.e. grey level histogram.
Further, the computing formula of the weighted mean value av of described grey scale pixel value is
, in the formula,
In the presentation video
The gray level of individual pixel correspondence,
The grey level range of presentation video is the 14bit input, promptly generally speaking
Further, the calculating of described gray scale sample range [a, b] is rule of thumb to set limit value a_min earlier, and b_max is then according to formula
,
,
, a, the b value is
The gray level of correspondence when satisfying condition.
Further, described greyscale transformation formula is
In the formula
Be the original gray level of pixel,
Be the gray level after proofreading and correct.
The adaptive line conversion Enhancement Method of a kind of infrared image of the present invention has made full use of the intensity profile information of present image, chosen the higher dynamic range of grey level distribution probability, for [a, b] outside the gray level of only a few replace with a or b respectively, the dynamic calculation of weighted mean value av has guaranteed that transformation curve can carry out adaptive parameter adjustment according to the different images picture, thereby has improved the observing effect of whole image.The present invention has made full use of the information of image scene and intensity profile, both maximally utilised the intensity profile of object scene, forgone the to greatest extent again interference of ground unrest, the figure image intensifying that both had been applicable to the specific objective scene is proofreaied and correct, be applicable to the real-time adaptive figure image intensifying of scene on the same stage again, thereby improve the usable range of linear transformation method greatly and strengthen effect.
Description of drawings
Fig. 1 is the process flow diagram of the inventive method embodiment;
Fig. 2 is the segmented conversion curve map of the embodiment of the invention;
Fig. 3 is the original input figure of the embodiment of the invention;
Fig. 4 is the design sketch after the process enhancement process of the embodiment of the invention.
Embodiment
The adaptive line conversion Enhancement Method of a kind of infrared image of the present invention is utilized the image histogram statistics, and the dynamic calculation of gray scale sample range and pixel weighting draw value realizes linear enhancing of real-time adaptive of image.Index transfer process in this method is optimized Algorithm as far as possible, improves operational efficiency.Key of the present invention is to utilize the dynamic calculation of gray scale sample range [a, b] and pixel weighting draw value av to realize the computing method of the real-time update of correction parameter.
Method flow diagram of the present invention according to the statistics with histogram of a width of cloth infrared image gray-scale value, calculates the wherein weighted mean value av of all grey scale pixel values as shown in Figure 1, gray scale sample range [a, b] in conjunction with calculating obtains corresponding to the point of three in the rectangular coordinate system on the histogram (a, 0), (av, 127), (b, 255), get segmentation according to these 3 and gray-scale value is cut apart chosen, current picture is carried out greyscale transformation, to realize the enhancing to image, concrete steps are as follows:
1, suppose to get the image of k frame, establish it and be of a size of Lx(k) * Ly(k), wherein, Lx (k) is the length of k two field picture, Ly (k) is the width of k two field picture.
2, at first add up the grey level histogram of all pixels of k two field picture, its formula is as follows:
In the formula, have the sum of pixel in the n presentation video,
In the presentation video
The number of times that individual gray level occurs, L represents that the total number of gray level is Lx(k) * Ly(k),
Expression the
The probability of level gray level, i.e. grey level histogram.
3, obtain the weighted mean value of all grey scale pixel values
, and gray scale sample range border limit value a, b; Formula is as follows:
In the formula,
In the presentation video
The gray level of individual pixel correspondence,
The grey level range of presentation video is the 14bit input, promptly generally speaking
Calculating gray scale sample range border limit value a, during b, at first rule of thumb set limit value
, then according to formula:
A, the b value is
The gray level of correspondence when satisfying condition.
4, at definite a, b after the value of av, can obtain corresponding to the point of three in the rectangular coordinate system on the histogram
,
,
, get segmentation according to these 3 and gray-scale value is cut apart chosen, as shown in Figure 2, to use this segmented conversion curve current picture is carried out greyscale transformation, formula is as follows:
In the formula,
Be the original gray level of pixel,
Be the gray level after proofreading and correct.
After the 14bit gray level carried out linear transformation and be converted to the 8bit gray level, the image after the output conversion was finished the enhancement process of infrared image, and effect as shown in Figure 3, Figure 4.
Claims (5)
1. the adaptive line conversion Enhancement Method of an infrared image is characterized in that this method is according to the statistics with histogram of an infrared image gray-scale value, calculate the wherein weighted mean value av of all grey scale pixel values, in conjunction with the gray scale sample range of calculating [a, b], obtain corresponding to the point of three in the rectangular coordinate system on the histogram (a, 0), (av, 127), (b, 255) are got segmentation according to these 3 and gray-scale value is cut apart are chosen, current picture is carried out greyscale transformation, to realize enhancing to image.
2. the adaptive line conversion Enhancement Method of a kind of infrared image according to claim 1, it is characterized in that: a described infrared image is the k two field picture, be of a size of Lx(k) * Ly(k), Lx (k), Ly (k) are respectively the length and the width of k two field picture, add up the grey level histogram of all pixels of k two field picture, formula is
, j=0,1,2 ..., L-1; In the formula, have the sum of pixel in the n presentation video,
In the presentation video
The number of times that individual gray level occurs, L represents that the total number of gray level is Lx(k) * Ly(k),
Expression the
The probability of level gray level, i.e. grey level histogram.
3. the adaptive line conversion Enhancement Method of a kind of infrared image according to claim 2, it is characterized in that: the computing formula of the weighted mean value av of described grey scale pixel value is
, in the formula,
In the presentation video
The gray level of individual pixel correspondence,
The grey level range of presentation video is the 14bit input, promptly generally speaking
4. the adaptive line conversion Enhancement Method of a kind of infrared image according to claim 3 is characterized in that: the calculating of described gray scale sample range [a, b] is rule of thumb to set limit value a_min earlier, and b_max is then according to formula
,
,
, a, the b value is
The gray level of correspondence when satisfying condition.
5. the adaptive line conversion Enhancement Method of a kind of infrared image according to claim 4, it is characterized in that: described greyscale transformation formula is
In the formula
Be the original gray level of pixel,
Be the gray level after proofreading and correct.
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Cited By (11)
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| CN102831592A (en) * | 2012-08-10 | 2012-12-19 | 中国电子科技集团公司第四十一研究所 | Image nonlinearity enhancement method based on histogram subsection transformation |
| CN103473783A (en) * | 2013-09-27 | 2013-12-25 | 贵州电力试验研究院 | Image evaluation method for preventing missing detection of GIS equipment X-ray detection result |
| CN104268845A (en) * | 2014-10-31 | 2015-01-07 | 北京津同利华科技有限公司 | Self-adaptive double local reinforcement method of extreme-value temperature difference short wave infrared image |
| CN104899833A (en) * | 2014-03-07 | 2015-09-09 | 安凯(广州)微电子技术有限公司 | Image defogging method and device |
| CN105046669A (en) * | 2015-08-19 | 2015-11-11 | 中国航空工业集团公司北京长城航空测控技术研究所 | Method for enhancing high-speed acquired images of line-scan digital camera |
| CN105488774A (en) * | 2015-12-05 | 2016-04-13 | 中国航空工业集团公司洛阳电光设备研究所 | Gray transformation method and device for image display |
| CN107945122A (en) * | 2017-11-07 | 2018-04-20 | 武汉大学 | Infrared image enhancing method and system based on self-adapting histogram segmentation |
| CN108319966A (en) * | 2017-10-13 | 2018-07-24 | 西安科技大学 | The method for identifying and classifying of equipment in a kind of substation's complex background infrared image |
| CN111951194A (en) * | 2020-08-26 | 2020-11-17 | 重庆紫光华山智安科技有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN112771568A (en) * | 2020-04-23 | 2021-05-07 | 深圳市大疆创新科技有限公司 | Infrared image processing method, device, movable platform and computer readable medium |
| CN113284069A (en) * | 2021-06-08 | 2021-08-20 | 西安羲和永青医疗科技有限责任公司 | Image processing method for handheld human body orthopedic DR |
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102831592B (en) * | 2012-08-10 | 2015-12-16 | 中国电子科技集团公司第四十一研究所 | Based on the image nonlinearity enhancement method of histogram subsection transformation |
| CN102831592A (en) * | 2012-08-10 | 2012-12-19 | 中国电子科技集团公司第四十一研究所 | Image nonlinearity enhancement method based on histogram subsection transformation |
| CN103473783A (en) * | 2013-09-27 | 2013-12-25 | 贵州电力试验研究院 | Image evaluation method for preventing missing detection of GIS equipment X-ray detection result |
| CN104899833B (en) * | 2014-03-07 | 2018-11-13 | 安凯(广州)微电子技术有限公司 | A kind of image defogging method and device |
| CN104899833A (en) * | 2014-03-07 | 2015-09-09 | 安凯(广州)微电子技术有限公司 | Image defogging method and device |
| CN104268845A (en) * | 2014-10-31 | 2015-01-07 | 北京津同利华科技有限公司 | Self-adaptive double local reinforcement method of extreme-value temperature difference short wave infrared image |
| CN105046669A (en) * | 2015-08-19 | 2015-11-11 | 中国航空工业集团公司北京长城航空测控技术研究所 | Method for enhancing high-speed acquired images of line-scan digital camera |
| CN105046669B (en) * | 2015-08-19 | 2017-07-25 | 中国航空工业集团公司北京长城航空测控技术研究所 | The Enhancement Method of line-scan digital camera high speed acquisition image |
| CN105488774A (en) * | 2015-12-05 | 2016-04-13 | 中国航空工业集团公司洛阳电光设备研究所 | Gray transformation method and device for image display |
| CN108319966A (en) * | 2017-10-13 | 2018-07-24 | 西安科技大学 | The method for identifying and classifying of equipment in a kind of substation's complex background infrared image |
| CN107945122A (en) * | 2017-11-07 | 2018-04-20 | 武汉大学 | Infrared image enhancing method and system based on self-adapting histogram segmentation |
| CN107945122B (en) * | 2017-11-07 | 2021-10-22 | 武汉大学 | Infrared image enhancement method and system based on self-adaptive histogram segmentation |
| CN112771568A (en) * | 2020-04-23 | 2021-05-07 | 深圳市大疆创新科技有限公司 | Infrared image processing method, device, movable platform and computer readable medium |
| WO2021212435A1 (en) * | 2020-04-23 | 2021-10-28 | 深圳市大疆创新科技有限公司 | Infrared image processing method and apparatus, and movable platform and computer readable medium |
| CN111951194A (en) * | 2020-08-26 | 2020-11-17 | 重庆紫光华山智安科技有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN111951194B (en) * | 2020-08-26 | 2024-02-02 | 重庆紫光华山智安科技有限公司 | Image processing method, apparatus, electronic device, and computer-readable storage medium |
| CN113284069A (en) * | 2021-06-08 | 2021-08-20 | 西安羲和永青医疗科技有限责任公司 | Image processing method for handheld human body orthopedic DR |
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Application publication date: 20111005 |