{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":682,"title":"Image Processing 002 : Fix Vignetting in a Visible Sensor","description":"The task is to correct image files for Visible scanning sensors that due to errant tolerancing produce vignetted(V) images. The V artifact is in a converging ray bundle and only the edges are impacted to differing amounts. The obscuration varies cross-scan but is constant for all samples. Sensor package repair is cost prohibitive. Luckily, the Image Processing is Matlab based and needs only a fast function to perform the V adjustment good enough for govt work.\r\n\r\nOutput: Fixed image(Fimg) (Array \u003c= 512x512 of type double)\r\n\r\nInput: Vignetted Image file(Vimg) (double); Obscuration (1xncol)\r\n\r\nTolerance: Good enough is 1%+1 count of Truth (Timg)\r\n\r\nObscuration: Max is 25% at edge, Min 8 columns wide.\r\n\r\nVignetted Image creation: Start with Truth (Timg)\r\n\r\nVimg = Timg(1-Obscuration)\r\n\r\nexample: Obs = [.25 .1 0...0 .1 .25]; obs [50 20 0...0 20 50];\r\n\r\nload penny.mat; Timg=double(P);\r\n\r\nVimg=Timg.*repmat(1-Obs,128,1)\r\n\r\n\r\nThe Cody \"Image processing 001\" for an IR and using In-scene data was not intended to be so complex. This and its successor should have appeared prior to \"001\".\r\n\r\nThis Visible Vignetting correction is very direct.","description_html":"\u003cp\u003eThe task is to correct image files for Visible scanning sensors that due to errant tolerancing produce vignetted(V) images. The V artifact is in a converging ray bundle and only the edges are impacted to differing amounts. The obscuration varies cross-scan but is constant for all samples. Sensor package repair is cost prohibitive. Luckily, the Image Processing is Matlab based and needs only a fast function to perform the V adjustment good enough for govt work.\u003c/p\u003e\u003cp\u003eOutput: Fixed image(Fimg) (Array \u0026lt;= 512x512 of type double)\u003c/p\u003e\u003cp\u003eInput: Vignetted Image file(Vimg) (double); Obscuration (1xncol)\u003c/p\u003e\u003cp\u003eTolerance: Good enough is 1%+1 count of Truth (Timg)\u003c/p\u003e\u003cp\u003eObscuration: Max is 25% at edge, Min 8 columns wide.\u003c/p\u003e\u003cp\u003eVignetted Image creation: Start with Truth (Timg)\u003c/p\u003e\u003cp\u003eVimg = Timg(1-Obscuration)\u003c/p\u003e\u003cp\u003eexample: Obs = [.25 .1 0...0 .1 .25]; obs [50 20 0...0 20 50];\u003c/p\u003e\u003cp\u003eload penny.mat; Timg=double(P);\u003c/p\u003e\u003cp\u003eVimg=Timg.*repmat(1-Obs,128,1)\u003c/p\u003e\u003cp\u003eThe Cody \"Image processing 001\" for an IR and using In-scene data was not intended to be so complex. This and its successor should have appeared prior to \"001\".\u003c/p\u003e\u003cp\u003eThis Visible Vignetting correction is very direct.\u003c/p\u003e","function_template":"function Fimg = Vignette_fix(Vimg,obscuration)\r\n  Fimg = Vimg;\r\nend","test_suite":"%%\r\n%Typical Vignette\r\n load penny.mat;\r\n Timg=double(P);\r\n Timg=Timg+250;\r\n [nr nc]=size(Timg);\r\n obscuration=zeros(1,nc);\r\n \r\n widthL=24;\r\n widthR=16;\r\n maxobsL=.16;\r\n maxobsR=.20;\r\n % ObscurationLinear \r\n obscuration(1:1+widthL)=maxobsL*(widthL-(0:widthL))/widthL;\r\n obscuration(end:-1:end-widthR)=maxobsR*(widthR-(0:widthR))/widthR;\r\n  \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))\r\n%%\r\n% Low Image Signal\r\n load penny.mat;\r\n Timg=double(P);\r\n Timg=Timg+50;\r\n [nr nc]=size(Timg);\r\n\r\n obscuration=zeros(1,nc);\r\n \r\n widthL=16;\r\n widthR=12;\r\n maxobsL=.08;\r\n maxobsR=.12;\r\n % ObscurationLinear \r\n obscuration(1:1+widthL)=maxobsL*(widthL-(0:widthL))/widthL;\r\n obscuration(end:-1:end-widthR)=maxobsR*(widthR-(0:widthR))/widthR;\r\n  \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))\r\n%%\r\n %Timg = double(imread('concordorthophoto.png')); % Matlab full image\r\n Timg =double(imread('example.tif')); % NGC 6543\r\n Timg=Timg(1:512,1:512);\r\n Timg=Timg+400; % Nominal 20\r\n [nr nc]=size(Timg);\r\n obscuration=zeros(1,nc);\r\n \r\n widthL=48;\r\n widthR=40;\r\n maxobsL=.2; % chan 1 Vig = 0.1842\r\n maxobsR=.15; % Chan 512 Vig = 0.1359\r\n % Obscuration 4th order \r\n obscuration(1:1+widthL)=maxobsL*(((1:1+widthL)-(1+widthL)).^4/(widthL+1)^4);\r\n obscuration(end-widthR:end)=maxobsR*(((widthR+1:-1:1)-(1+widthR)).^4/(widthR+1)^4);\r\n \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))\r\n%%\r\n% Timg = double(imread('concordorthophoto.png')); % Matlab full image\r\n%Timg=Timg(1600:2111,900:1411);\r\n Timg =double(imread('example.tif')); % NGC 6543\r\n Timg=Timg(31:542,81:592);\r\n Timg=Timg+300;\r\n [nr nc]=size(Timg);\r\n obscuration=zeros(1,nc);\r\n\r\n widthL=42;\r\n widthR=36;\r\n maxobsL=.18; % \r\n maxobsR=.23; % \r\n % Obscuration Linear \r\n obscuration(1:1+widthL)=maxobsL*(widthL-(0:widthL))/widthL;\r\n obscuration(end:-1:end-widthR)=maxobsR*(widthR-(0:widthR))/widthR;\r\n \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2017-10-16T23:49:38.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2012-05-12T18:32:26.000Z","updated_at":"2025-12-07T18:07:07.000Z","published_at":"2012-05-12T18:45:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe task is to correct image files for Visible scanning sensors that due to errant tolerancing produce vignetted(V) images. The V artifact is in a converging ray bundle and only the edges are impacted to differing amounts. The obscuration varies cross-scan but is constant for all samples. Sensor package repair is cost prohibitive. Luckily, the Image Processing is Matlab based and needs only a fast function to perform the V adjustment good enough for govt work.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: Fixed image(Fimg) (Array \u0026lt;= 512x512 of type double)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: Vignetted Image file(Vimg) (double); Obscuration (1xncol)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTolerance: Good enough is 1%+1 count of Truth (Timg)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eObscuration: Max is 25% at edge, Min 8 columns wide.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVignetted Image creation: Start with Truth (Timg)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVimg = Timg(1-Obscuration)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eexample: Obs = [.25 .1 0...0 .1 .25]; obs [50 20 0...0 20 50];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eload penny.mat; Timg=double(P);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVimg=Timg.*repmat(1-Obs,128,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Cody \\\"Image processing 001\\\" for an IR and using In-scene data was not intended to be so complex. This and its successor should have appeared prior to \\\"001\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Visible Vignetting correction is very direct.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":682,"title":"Image Processing 002 : Fix Vignetting in a Visible Sensor","description":"The task is to correct image files for Visible scanning sensors that due to errant tolerancing produce vignetted(V) images. The V artifact is in a converging ray bundle and only the edges are impacted to differing amounts. The obscuration varies cross-scan but is constant for all samples. Sensor package repair is cost prohibitive. Luckily, the Image Processing is Matlab based and needs only a fast function to perform the V adjustment good enough for govt work.\r\n\r\nOutput: Fixed image(Fimg) (Array \u003c= 512x512 of type double)\r\n\r\nInput: Vignetted Image file(Vimg) (double); Obscuration (1xncol)\r\n\r\nTolerance: Good enough is 1%+1 count of Truth (Timg)\r\n\r\nObscuration: Max is 25% at edge, Min 8 columns wide.\r\n\r\nVignetted Image creation: Start with Truth (Timg)\r\n\r\nVimg = Timg(1-Obscuration)\r\n\r\nexample: Obs = [.25 .1 0...0 .1 .25]; obs [50 20 0...0 20 50];\r\n\r\nload penny.mat; Timg=double(P);\r\n\r\nVimg=Timg.*repmat(1-Obs,128,1)\r\n\r\n\r\nThe Cody \"Image processing 001\" for an IR and using In-scene data was not intended to be so complex. This and its successor should have appeared prior to \"001\".\r\n\r\nThis Visible Vignetting correction is very direct.","description_html":"\u003cp\u003eThe task is to correct image files for Visible scanning sensors that due to errant tolerancing produce vignetted(V) images. The V artifact is in a converging ray bundle and only the edges are impacted to differing amounts. The obscuration varies cross-scan but is constant for all samples. Sensor package repair is cost prohibitive. Luckily, the Image Processing is Matlab based and needs only a fast function to perform the V adjustment good enough for govt work.\u003c/p\u003e\u003cp\u003eOutput: Fixed image(Fimg) (Array \u0026lt;= 512x512 of type double)\u003c/p\u003e\u003cp\u003eInput: Vignetted Image file(Vimg) (double); Obscuration (1xncol)\u003c/p\u003e\u003cp\u003eTolerance: Good enough is 1%+1 count of Truth (Timg)\u003c/p\u003e\u003cp\u003eObscuration: Max is 25% at edge, Min 8 columns wide.\u003c/p\u003e\u003cp\u003eVignetted Image creation: Start with Truth (Timg)\u003c/p\u003e\u003cp\u003eVimg = Timg(1-Obscuration)\u003c/p\u003e\u003cp\u003eexample: Obs = [.25 .1 0...0 .1 .25]; obs [50 20 0...0 20 50];\u003c/p\u003e\u003cp\u003eload penny.mat; Timg=double(P);\u003c/p\u003e\u003cp\u003eVimg=Timg.*repmat(1-Obs,128,1)\u003c/p\u003e\u003cp\u003eThe Cody \"Image processing 001\" for an IR and using In-scene data was not intended to be so complex. This and its successor should have appeared prior to \"001\".\u003c/p\u003e\u003cp\u003eThis Visible Vignetting correction is very direct.\u003c/p\u003e","function_template":"function Fimg = Vignette_fix(Vimg,obscuration)\r\n  Fimg = Vimg;\r\nend","test_suite":"%%\r\n%Typical Vignette\r\n load penny.mat;\r\n Timg=double(P);\r\n Timg=Timg+250;\r\n [nr nc]=size(Timg);\r\n obscuration=zeros(1,nc);\r\n \r\n widthL=24;\r\n widthR=16;\r\n maxobsL=.16;\r\n maxobsR=.20;\r\n % ObscurationLinear \r\n obscuration(1:1+widthL)=maxobsL*(widthL-(0:widthL))/widthL;\r\n obscuration(end:-1:end-widthR)=maxobsR*(widthR-(0:widthR))/widthR;\r\n  \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))\r\n%%\r\n% Low Image Signal\r\n load penny.mat;\r\n Timg=double(P);\r\n Timg=Timg+50;\r\n [nr nc]=size(Timg);\r\n\r\n obscuration=zeros(1,nc);\r\n \r\n widthL=16;\r\n widthR=12;\r\n maxobsL=.08;\r\n maxobsR=.12;\r\n % ObscurationLinear \r\n obscuration(1:1+widthL)=maxobsL*(widthL-(0:widthL))/widthL;\r\n obscuration(end:-1:end-widthR)=maxobsR*(widthR-(0:widthR))/widthR;\r\n  \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))\r\n%%\r\n %Timg = double(imread('concordorthophoto.png')); % Matlab full image\r\n Timg =double(imread('example.tif')); % NGC 6543\r\n Timg=Timg(1:512,1:512);\r\n Timg=Timg+400; % Nominal 20\r\n [nr nc]=size(Timg);\r\n obscuration=zeros(1,nc);\r\n \r\n widthL=48;\r\n widthR=40;\r\n maxobsL=.2; % chan 1 Vig = 0.1842\r\n maxobsR=.15; % Chan 512 Vig = 0.1359\r\n % Obscuration 4th order \r\n obscuration(1:1+widthL)=maxobsL*(((1:1+widthL)-(1+widthL)).^4/(widthL+1)^4);\r\n obscuration(end-widthR:end)=maxobsR*(((widthR+1:-1:1)-(1+widthR)).^4/(widthR+1)^4);\r\n \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))\r\n%%\r\n% Timg = double(imread('concordorthophoto.png')); % Matlab full image\r\n%Timg=Timg(1600:2111,900:1411);\r\n Timg =double(imread('example.tif')); % NGC 6543\r\n Timg=Timg(31:542,81:592);\r\n Timg=Timg+300;\r\n [nr nc]=size(Timg);\r\n obscuration=zeros(1,nc);\r\n\r\n widthL=42;\r\n widthR=36;\r\n maxobsL=.18; % \r\n maxobsR=.23; % \r\n % Obscuration Linear \r\n obscuration(1:1+widthL)=maxobsL*(widthL-(0:widthL))/widthL;\r\n obscuration(end:-1:end-widthR)=maxobsR*(widthR-(0:widthR))/widthR;\r\n \r\n Vimg=bsxfun(@times,Timg,1-obscuration);\r\n% Processing\r\n Fimg=Vignette_fix(Vimg,obscuration);\r\n%\r\n% Verification\r\n tolerance=[0.01 1];\r\n Pass=1;\r\n % Hi check\r\n tcheck=(Timg*(1+tolerance(1))+tolerance(2))-Fimg;\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n % Lo check\r\n tcheck=Fimg-(Timg*(1-tolerance(1))-tolerance(2));\r\n if min(tcheck(:))\u003c0,Pass=0;end\r\n \r\n assert(isequal(Pass,1))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2017-10-16T23:49:38.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2012-05-12T18:32:26.000Z","updated_at":"2025-12-07T18:07:07.000Z","published_at":"2012-05-12T18:45:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe task is to correct image files for Visible scanning sensors that due to errant tolerancing produce vignetted(V) images. The V artifact is in a converging ray bundle and only the edges are impacted to differing amounts. The obscuration varies cross-scan but is constant for all samples. Sensor package repair is cost prohibitive. Luckily, the Image Processing is Matlab based and needs only a fast function to perform the V adjustment good enough for govt work.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: Fixed image(Fimg) (Array \u0026lt;= 512x512 of type double)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: Vignetted Image file(Vimg) (double); Obscuration (1xncol)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTolerance: Good enough is 1%+1 count of Truth (Timg)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eObscuration: Max is 25% at edge, Min 8 columns wide.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVignetted Image creation: Start with Truth (Timg)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVimg = Timg(1-Obscuration)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eexample: Obs = [.25 .1 0...0 .1 .25]; obs [50 20 0...0 20 50];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eload penny.mat; Timg=double(P);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVimg=Timg.*repmat(1-Obs,128,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Cody \\\"Image processing 001\\\" for an IR and using In-scene data was not intended to be so complex. 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