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CN104200430A - Image region stray light eliminating device based on moment matching and method thereof - Google Patents

Image region stray light eliminating device based on moment matching and method thereof Download PDF

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CN104200430A
CN104200430A CN201410413131.2A CN201410413131A CN104200430A CN 104200430 A CN104200430 A CN 104200430A CN 201410413131 A CN201410413131 A CN 201410413131A CN 104200430 A CN104200430 A CN 104200430A
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岳春宇
何红艳
邢坤
刘爽
鲍云飞
周楠
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Beijing Research Institute of Mechanical and Electrical Technology
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Abstract

一种基于矩匹配的图像区域杂散光消除装置及其方法，该装置根据图像灰度信息的统计规律，由整幅图像的灰度变化趋势，获得图像矩匹配计算的参考均值和参考方差，并进行标准矩匹配处理。该方法针对图像内部区域有杂散光影响的均匀地物遥感图像，能够较好去除杂散光的影响，保持图像灰度行方向均匀过渡。该方法能够有效地复原影像、改善影像质量，算法效率也很高。

A device and method for eliminating stray light in an image area based on moment matching. According to the statistical law of image gray information, the device obtains the reference mean and reference variance calculated by image moment matching from the gray change trend of the entire image, and Perform standard moment matching processing. This method is aimed at remote sensing images of uniform ground objects affected by stray light in the inner area of the image, which can better remove the influence of stray light and maintain a uniform transition in the image grayscale row direction. This method can effectively restore the image and improve the image quality, and the algorithm efficiency is also very high.

Description

A kind of image-region parasitic light cancellation element and method thereof based on square coupling
Technical field
The present invention relates to a kind of image-region parasitic light cancellation element and method thereof based on square coupling, belong to field of remote sensing image processing.
Background technology
Due to the impact of parasitic light, changed the half-tone information of image, the especially impact on image local area, brings difficulty to interpretation and the application of satellite image.Be necessary the parasitic light of satellite image to analyze, eliminate, thereby guarantee the image quality of satellite optics useful load.
The work that most of parasitic light suppresses is to realize by Optical System Design.The source of research parasitic light, thus carry out the analysis of geometrical optics policy, be to eliminate the most basic method of parasitic light.When space camera is in orbit time, the parasitic light causing for design careless omission or due to complex space environment, can only eliminate by the image processing method of rear end.Formerly educate the satellite imagery data that triumphant grade is utilized particular moment, separated actual signal and parasitic light signal, generate the parasitic light template in each moment, and the image processing method by deconvolution carries out parasitic light inhibition to FY-2 radiometer visible images.But the method is only applicable to geo-stationary meteorological satellite, for middle high-resolution earth observation satellite data, cannot isolate parasitic light signal according to particular moment imaging data.
The method directly parasitic light being suppressed based on image area is at present less, and the method not suppressing for middle high-resolution earth observation satellite image area parasitic light in actual applications.For the stray light that in middle high-resolution remote sensing images, radiant correction cannot be removed, also cannot solve.
Summary of the invention
The technical matters that the present invention solves is: overcome prior art deficiency, the even atural object remote sensing images that have stray light for image interior zone, a kind of image-region parasitic light cancellation element and method thereof based on square coupling is provided, according to the statistical law of gradation of image information, grey scale change trend by entire image, find reference average and variance that image moment coupling is calculated, the column criterion of going forward side by side square matching treatment, soon average and the variance of the gray scale of each row pixel of image are adjusted into reference to average and variance, solve middle high-resolution earth observation satellite image graph image field parasitic light and eliminated problem, improve remote sensing image processing result radiation quality.
The technical scheme that the present invention solves is: a kind of image-region parasitic light cancellation element based on square coupling, comprise that image information statistical module and region parasitic light suppress module, image information statistical module comprises data preparatory unit and data analysis unit, and parasitic light suppresses module and comprises square matching unit and result output unit; Data preparatory unit reads row value R and the train value C of pending image I, and the gray-scale value of each pixel, pending image I is middle high-resolution remote sensing images, according to the gray-scale value of each row pixel of pending image I, calculate gray-scale value average and the variance of this each row pixel of pending image I, and send pending image and each row gray-scale value average thereof and variance to data analysis unit; Data analysis unit is the reference average threshold value T of setting data analytic unit first c, first by the gray-scale value average mean of the first row pixel of pending image I c1be assigned to the reference average mean of data analysis unit rwith effective average and mean use, the gray-scale value variances sigma of the first row pixel of pending image I c1be assigned to the reference variances sigma of data analysis unit rwith effective variance and σ use, then ask the i row gray-scale value average of pending image I to add the reference average mean of data analysis unit rmean value, this mean value is assigned to again to the reference average mean of data analysis unit rif reference average is now less than or equal to reference to average threshold value T c, by the gray-scale value average mean of the i row pixel of pending image I ciwith gray-scale value variances sigma cibe added to respectively data analysis unit effective average and mean usewith effective variance and σ usei is more than or equal to 2 positive integer, until travel through after all row of pending image, and the total how many row gray-scale value averages of record pending image I except first row and gray-scale value variance be added to effective average and with effective variance with, this columns is designated as to effective columns; Travel through effective average after all row of pending image I and mean usewith effective variance and σ usedivided by effective columns, obtain final reference average and final reference variance, send pending image I, final reference average and final reference variance to square matching unit; The final reference average that square matching unit sends according to data analysis unit is carried out standard square with final reference variance to pending image I and is mated calculating, obtain region parasitic light removal of images, and region parasitic light removal of images is transferred to result output unit; Result output unit converts region parasitic light removal of images to image file and exports and show.
An image-region parasitic light removing method based on square coupling, step is as follows:
(1) data preparatory unit is obtained pending image I, and obtains the row value R of pending image I and the gray-scale value of train value C and each pixel;
(2) data preparatory unit is calculated each row I of pending image I cigradation of image value average mean ciwith variances sigma ci, i is positive integer and i≤C;
(3) data analysis unit is set with reference to average threshold value T c, order is with reference to average mean r=mean c1, with reference to variances sigma r=σ c1; Make effective average and mean use=mean c1, effectively variance and σ use=σ c1; Make effective columns n=1;
(4) the pending image i row I that data analysis unit calculates in step (2) cigrey scale pixel value average mean ci, gray-scale value variances sigma ci, calculate new reference average mean ri=(mean r+ mean ci)/2, if fabs is (mean ri-mean r)≤T c, new effective average and mean usei=mean use+ mean ci, effectively variance and σ usei=σ use+ σ ci, effectively columns n value increases by 1, if fabs is (mean ri-mean r) >T c, choose pending image I I c (i+1)row I c (i+1)calculate, i is more than or equal to 2 and be less than or equal to the positive integer of C-1;
(5) data analysis unit repeating step (4), until all row traversal finishes in pending image I, obtain effective average and mean useC, effectively variance and σ useCwith effective columns n value, final reference average mean rC=mean useC/ n, final reference variances sigma rC=σ useC/ n;
(6) square matching unit travels through each row of pending image I, the final reference average mean obtaining in step (5) rCwith final reference variances sigma rC, according to standard square matching algorithm, recalculate the gray-scale value of this row image, obtain region parasitic light removal of images;
(7) the region parasitic light removal of images I' that the output of result output unit is obtained by step (6).
The present invention's advantage is compared with prior art:
(1) parasitic light at image area, space camera being caused due to design careless omission or complex space environment in orbit time suppresses, and has avoided the situation analysis in orbit of complicated space environment and camera system;
(2) in data analysis unit, travel through by column pending image column gray average, the variation of analysis image gray scale, find Gray Level Jump region, when calculating square matching parameter, do not adopt the information in this region, guarantee when image parasitic light suppresses to remove preferably stray light region.
(3) while calculating square matching parameter, with reference to average, calculate by column, keep gradation of image line direction even transition, reconstructed images, does not have Gray Level Jump phenomenon in parasitic light inhibition result images effectively.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the embodiment of the present invention;
Fig. 2 is apparatus structure schematic diagram of the present invention.
Embodiment
Basic ideas of the present invention are: a kind of image-region parasitic light cancellation element and method thereof based on square coupling is provided, for image interior zone, there are the even atural object remote sensing images of stray light according to the statistical law of gradation of image information, grey scale change trend by entire image, obtain reference average and variance that image moment coupling is calculated, the column criterion of going forward side by side square matching treatment, solve middle high-resolution earth observation satellite image graph image field parasitic light and eliminated problem, improved remote sensing image processing result radiation quality.
Below in conjunction with accompanying drawing, the present invention is described in further detail, as shown in Figure 2, a kind of image-region parasitic light cancellation element based on square coupling, comprise that image information statistical module and region parasitic light suppress module, image information statistical module comprises data preparatory unit and data analysis unit, and parasitic light suppresses module and comprises square matching unit and result output unit; Data preparatory unit reads row value R and the row C value of pending image I, and the gray-scale value of each pixel, pending image I is middle high-resolution remote sensing images, according to the gray-scale value of each row pixel of pending image I, calculate gray-scale value average and the variance of this each row pixel of pending image I, and send pending image and each row gray-scale value average thereof and variance to data analysis unit; Data analysis unit is the reference average threshold value T of setting data analytic unit first c, first by the gray-scale value average mean of the first row pixel of pending image I c1be assigned to the reference average mean of data analysis unit rwith effective average and mean use, the gray-scale value variances sigma of the first row pixel of pending image I c1be assigned to the reference variances sigma of data analysis unit rwith effective variance and σ use, then ask the i row gray-scale value average of pending image I to add the reference average mean of data analysis unit rmean value, this mean value is assigned to again to the reference average mean of data analysis unit rif reference average is now less than or equal to reference to average threshold value T c, by the gray-scale value average mean of the i row pixel of pending image I ciwith gray-scale value variances sigma cibe added to respectively effective average and the mean of data analysis unit usewith effective variance and σ usei is more than or equal to 2 positive integer, until travel through after all row of pending image, and the total how many row gray-scale value averages of record pending image I except first row and gray-scale value variance be added to effective average and with effective variance with, this columns is designated as to effective columns; Travel through effective average and mean after all row of pending image I usewith effective variance and σ usedivided by effective columns, obtain final reference average and final reference variance, send pending image I, final reference average and final reference variance to square matching unit; The final reference average that square matching unit sends according to data analysis unit is carried out standard square with final reference variance to pending image I and is mated calculating, obtain region parasitic light removal of images, and region parasitic light removal of images is transferred to result output unit; Result output unit converts region parasitic light removal of images to image file and exports and show.
Utilize a kind of image-region parasitic light removing method based on square coupling of above-mentioned a kind of image-region parasitic light cancellation element based on square coupling, as shown in Figure 1, pending image is the even atural object remote sensing images that image interior zone has stray light, and this method concrete steps are as follows:
(1) data preparatory unit is obtained pending image I, and obtains the row value R=2865 of pending image I and the gray-scale value of train value C=3036 and each pixel;
(2) data preparatory unit is calculated each row gradation of image value average mean of pending image I ciwith variances sigma ci, i is positive integer and i≤C;
(3) data analysis unit is set with reference to average threshold value T c=5, for even remote sensing images adjacent column average, differ larger like this, represent that half-tone information has sudden change, does not meet actual conditions.General middle high-resolution remote sensing images stray light region is inner at image, so the gray-scale value of image border is influenced less, image makes with reference to average mean r=mean c1=338.034, with reference to variances sigma r=σ c1=15.248; Make effective average and mean use=mean c1=338.034, effectively variance and σ use=σ c1=15.248; Due to pending image I first row gray average and variance directly add effective average and with effective variance and, make effective columns n=1;
(4) the pending image secondary series I that data analysis unit calculates in step (2) c2grey scale pixel value average mean c2=337.965, gray-scale value variances sigma c2=15.224, calculate new reference average mean r2=(mean r+ mean c2)/2=338.0, if fabs is (mean r2-mean r)=0.334<T c=5, the not sudden change of this row gradation of image is described, for even remote sensing images gray scale, along column direction, be generally to seamlessly transit, so this row image is not subject to the impact of parasitic light, can be used as the parameter that parasitic light suppresses, new effective average and mean use2=mean use+ mean c2=338.034+337.965=675.999, effectively variance and σ use2=σ use+ σ c2=15.248+15.224=30.472, n value increases by 1, if not, chooses pending image the 3rd row I c3calculate;
(5) data analysis unit repeating step (4), until all row traversal finishes in pending image I, obtain effective average and mean useC=944899, effectively variance and σ useC=49788 with effective columns n=2766, final reference average mean r=mean useC/ n=341.612, final reference variances sigma r=σ useC/ n=18.0, each row image statistics that is not subject to stray light by full width obtains the parameter that square coupling needs.
(6) square matching unit travels through each row of pending image I, the final reference average mean obtaining in step (5) rCwith final reference variances sigma rC, according to standard square matching algorithm, recalculate the gray-scale value of this row image, obtain region parasitic light removal of images.
(7) the region parasitic light removal of images I' that the output of result output unit is obtained by step (6).
Non-elaborated part of the present invention belongs to techniques well known.

Claims (2)

1.一种基于矩匹配的图像区域杂散光消除装置，其特征在于：包括图像信息统计模块和区域杂散光抑制模块，图像信息统计模块包括数据准备单元和数据分析单元，杂散光抑制模块包括矩匹配单元和结果输出单元；数据准备单元读取待处理图像I的行值R和列值C,及每个像素的灰度值，待处理图像I为中高分辨率遥感图像，根据待处理图像I每一列像素的灰度值计算该待处理图像I每一列像素的灰度值均值与方差，并将待处理图像及其各列灰度值均值与方差传送给数据分析单元；数据分析单元首先设定数据分析单元的参考均值阈值TC，先将待处理图像I的第一列像素的灰度值均值meanC1赋给数据分析单元的参考均值meanr与有效均值的和meanuse，待处理图像I的第一列像素的灰度值方差σC1赋给数据分析单元的参考方差σr与有效方差的和σuse，再求待处理图像I的第i列灰度值均值加上数据分析单元的参考均值meanr的平均值，将该平均值重新赋给数据分析单元的参考均值meanr，若此时的参考均值小于等于参考均值阈值TC，则将待处理图像I的第i列像素的灰度值均值meanCi和灰度值方差σCi分别累加到数据分析单元的有效均值的和meanuse与有效方差和σuse，i为大于等于2的正整数，直至遍历待处理图像的所有列后，并记录除第一列外待处理图像I共有多少列灰度值均值和灰度值方差累加到有效均值和与有效方差和，将该列数记为有效列数；遍历待处理图像I的所有列后的有效均值的和meanuse与有效方差和σuse除以有效列数，得到最终的参考均值与最终的参考方差，将待处理图像I、最终的参考均值与最终的参考方差传送给矩匹配单元；矩匹配单元根据数据分析单元传送来的最终的参考均值与最终的参考方差对待处理图像I进行标准矩匹配计算，得到区域杂散光消除图像，并将区域杂散光消除图像传输给结果输出单元；结果输出单元将区域杂散光消除图像转换成图像文件输出并显示。1. An image area stray light elimination device based on moment matching, characterized in that: it includes an image information statistics module and an area stray light suppression module, the image information statistics module includes a data preparation unit and a data analysis unit, and the stray light suppression module includes a moment Matching unit and result output unit; the data preparation unit reads the row value R and column value C of the image I to be processed, and the gray value of each pixel, the image I to be processed is a medium-high resolution remote sensing image, according to the image I to be processed The gray value of each row of pixels calculates the gray value mean and variance of each row of pixels of the image I to be processed, and the image to be processed and its gray value mean and variance of each column are sent to the data analysis unit; the data analysis unit first sets To determine the reference mean threshold T C of the data analysis unit, first assign the gray value mean C1 of the first column of pixels of the image I to be processed to the sum mean use of the reference mean r and the effective mean value of the data analysis unit, and the image to be processed The variance σ C1 of the gray value of the pixels in the first column of I is assigned to the sum of the reference variance σ r and the effective variance σ use of the data analysis unit, and then calculate the mean value of the gray value of the i-th column of the image I to be processed plus the data analysis unit The average value of the reference mean value mean r , and reassign the average value to the reference mean value mean r of the data analysis unit. If the reference mean value at this time is less than or equal to the reference mean value threshold T C , then the ith column pixel of the image I to be processed The gray value mean Ci and gray value variance σ Ci of the gray value are respectively added to the effective mean sum mean use and the effective variance sum σ use of the data analysis unit, i is a positive integer greater than or equal to 2, until traversing all the images to be processed After the column, record how many columns of gray value mean value and gray value variance are added to the effective mean value and the effective variance sum of the image I to be processed except the first column, and record the column number as the effective column number; traverse the image to be processed The sum of the effective mean and mean use and effective variance and σuse after all columns of I are divided by the number of effective columns to obtain the final reference mean and final reference variance, and the image I to be processed, the final reference mean and the final reference variance Send to the moment matching unit; the moment matching unit performs standard moment matching calculation on the image I to be processed according to the final reference mean and the final reference variance transmitted by the data analysis unit, obtains the regional stray light elimination image, and transmits the regional stray light elimination image to the result output unit; the result output unit converts the regional stray light elimination image into an image file for output and display. 2.一种基于矩匹配的图像区域杂散光消除方法，其特征在于步骤如下：2. A method for eliminating stray light in an image area based on moment matching, characterized in that the steps are as follows: (1)数据准备单元获取待处理图像I，并得到待处理图像I的行值R和列值C及每个像素的灰度值；(1) The data preparation unit acquires the image I to be processed, and obtains the row value R and the column value C of the image I to be processed and the gray value of each pixel; (2)数据准备单元计算待处理图像I每一列Ici图像灰度值均值meanCi与方差σCi，i为正整数且i≤C；(2) The data preparation unit calculates the average value mean Ci and variance σ Ci of the image gray value of each column Ici of the image I to be processed, i is a positive integer and i≤C; (3)数据分析单元设定参考均值阈值TC，令参考均值meanr＝meanC1，参考方差σr＝σC1；令有效均值的和meanuse＝meanC1，有效方差的和σuse＝σC1；令有效列数n＝1；(3) The data analysis unit sets the reference mean threshold T C , and sets the reference mean mean r = mean C1 , and the reference variance σ r = σ C1 ; sets the sum of effective mean values = mean C1 , and the sum of effective variances σ use = σ C1 ; Make the number of effective columns n=1; (4)数据分析单元由步骤(2)中计算得到的待处理图像第i列Ici的像素灰度值均值meanCi，灰度值方差σCi，计算新的参考均值meanri＝(meanr+meanCi)/2，若fabs(meanri-meanr)≤TC，则新的有效均值的和meanusei＝meanuse+meanCi，有效方差的和σusei＝σuse+σCi，有效列数n值增加1，若fabs(meanri-meanr)>TC，则选取待处理图像I第Ic(i+1)列IC(i+1)进行计算，i为大于等于2且小于等于C-1的正整数；(4) The data analysis unit calculates the new reference mean value mean ri = (mean r +mean Ci )/2, if fabs(mean ri -mean r )≤T C , then the new effective mean sum mean usei =mean use +mean Ci , the effective variance sum σ usei =σ use +σ Ci , effective The number of columns n increases by 1, if fabs(mean ri -mean r )>T C , select the column I C(i+1) of the image I to be processed for calculation, and i is greater than or equal to 2 and a positive integer less than or equal to C-1; (5)数据分析单元重复步骤(4)，直到待处理图像I中所有列遍历结束，得到有效均值的和meanuseC、有效方差的和σuseC与有效列数n值，最终的参考均值meanrC＝meanuseC/n，最终的参考方差σrC＝σuseC/n；(5) The data analysis unit repeats step (4) until the traversal of all columns in the image I to be processed is completed, and the sum mean useC of the effective mean value, the sum σ useC of the effective variance and the number of effective columns n are obtained, and the final reference mean value mean rC = mean useC /n, the final reference variance σ rC = σ useC /n; (6)矩匹配单元遍历待处理图像I每一列，由步骤(5)中获得的最终的参考均值meanrC和最终的参考方差σrC，根据标准矩匹配算法，重新计算该列图像的灰度值，得到区域杂散光消除图像；(6) The moment matching unit traverses each column of the image I to be processed, and recalculates the grayscale of the column image according to the standard moment matching algorithm based on the final reference mean rC and the final reference variance σ rC obtained in step (5). value, to get the regional stray light elimination image; (7)结果输出单元输出由步骤(6)得到的区域杂散光消除图像I'。(7) The result output unit outputs the regional stray light eliminated image I' obtained in step (6).
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CN108120680A (en) * 2017-12-19 2018-06-05 清华大学 The stray light minimizing technology and device of micro-imaging based on photoelectric characteristic priori
CN114140354A (en) * 2021-11-27 2022-03-04 河南中光学集团有限公司 A real-time method for removing vertical streaks from infrared images using improved local moment matching

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
F. L. GADALLAH等: "Destriping multisensor imagery with moment matching", 《INTERNATIONAL JOURNAL OF REMOTE SENSING》 *
刘正军等: "成像光谱仪图像条带噪声去除的改进矩匹配方法", 《遥感学报》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108120680A (en) * 2017-12-19 2018-06-05 清华大学 The stray light minimizing technology and device of micro-imaging based on photoelectric characteristic priori
CN108120680B (en) * 2017-12-19 2019-11-22 清华大学 Method and device for removing stray light in microscopic imaging based on prior photoelectric characteristics
CN114140354A (en) * 2021-11-27 2022-03-04 河南中光学集团有限公司 A real-time method for removing vertical streaks from infrared images using improved local moment matching

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