Tikhonov正则化超分辨率重建

% d=PyramidalLKOpticalFlow(img1, img2, roi) % % Computes the Lucas-Kanade optical flow using a gaussian pyramid. % The function builds a pyramid for images 1 and 2 and computes the flow % in each level, and using the flow an lower levels as initial estimates % for the flow in higher levels. The flow is computed as the displacement % from img2 to img1. % % Inputs: % img1 - The source image. % img2 - The destination image. % roi - A region in img1 in which the flow should be computed. % % Outpus: % d - The computed affine transformation parameters function d=PyramidalLKOpticalFlow(img1, img2, roi) img1=double(img1); img2=double(img2); roiSize = 1+roi(3:4)-roi(1:2); % Compute boundry for number of levels. We define two bounds. K<=6. In most % cases more than 6 levels is not needed => the image becomes too small. % Also, the roi at level k should not be too small. We define here that roi % should not become less than 4x4 pixels. % % Since the size of the ROI at level k is Roiwidth/2^k we have % Roiwidth/2^k >= 4 => 2^k<=Roiwidth/4 => k<log2(Roiwidth/4) = % log2(Roiwidth)-2 levels = min([floor(log2(min(roiSize))-2), 6]); % Compute Pyramids for image 1 and 2 pyramid1 = GuassianPyramid(img1, levels); pyramid2 = GuassianPyramid(img2, levels); % Set d to an empty affine displacement transformation d=zeros(2,3); % Start with initial translation 0 at lowest pyramid for l=levels:-1:1 disp(sprintf('Computing optical flow at level %u', l)); % Transform displacement to current level d(:,3)=sum(d,2)+d(:,3); % Compute current location of ROI tl=ceil(roi(1:2)./2^(l-1)); % Compute current size of ROI sz=floor(roiSize./2^(l-1)); % Make sure origin is at least 2 tl=max(tl, [2 2]); br=min(tl+sz-1, size(pyramid1{l})-1); % Compute displacement at current level d=IterativeLKOpticalFlow(pyramid1{l}, pyramid2{l}, [tl br], d); end