init

evalUnwarp.m

@@ -0,0 +1,102 @@
+function [ms, ld, li_d, wv, wh] = evalUnwarp(A, ref, data)
+%EVALUNWARP compute MSSSIM and LD between the unwarped image and the scan
+%   A:      unwarped image
+%   ref:    reference image, the scan image
+%   ms:     returned MS-SSIM value
+%   ld:     returned local distortion value
+%   Matlab image processing toolbox is necessary to compute ssim. The weights 
+%   for multi-scale ssim is directly adopted from:
+%
+%   Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. "Multiscale structural 
+%   similarity for image quality assessment." In Signals, Systems and Computers, 
+%   2004. Conference Record of the Thirty-Seventh Asilomar Conference on, 2003. 
+%
+%   Local distortion relies on the paper:
+%   Liu, Ce, Jenny Yuen, and Antonio Torralba. "Sift flow: Dense correspondence 
+%   across scenes and its applications." In PAMI, 2010.
+%
+%   and its implementation:
+%   https://people.csail.mit.edu/celiu/SIFTflow/
+
+x = A;
+y = ref;
+
+im1=imresize(imfilter(y,fspecial('gaussian',7,1.),'same','replicate'),0.5,'bicubic');
+im2=imresize(imfilter(x,fspecial('gaussian',7,1.),'same','replicate'),0.5,'bicubic');
+
+im1=im2double(im1);
+im2=im2double(im2);
+
+cellsize=3;
+gridspacing=1;
+
+sift1 = mexDenseSIFT(im1,cellsize,gridspacing);
+sift2 = mexDenseSIFT(im2,cellsize,gridspacing);
+
+SIFTflowpara.alpha=2*255;
+SIFTflowpara.d=40*255;
+SIFTflowpara.gamma=0.005*255;
+SIFTflowpara.nlevels=4;
+SIFTflowpara.wsize=2;
+SIFTflowpara.topwsize=10;
+SIFTflowpara.nTopIterations = 60;
+SIFTflowpara.nIterations= 30;
+
+
+[vx,vy,~]=SIFTflowc2f(sift1,sift2,SIFTflowpara);
+
+rows1p = size(im1,1);
+cols1p = size(im1,2);
+
+% Li-D
+rowstd_sum = 0;
+for i = 1:rows1p
+    rowstd = std(vy(i, :),1); 
+    rowstd_sum = rowstd_sum + rowstd;
+end
+rowstd_mean = rowstd_sum / rows1p;
+
+colstd_sum = 0;
+for i = 1:cols1p
+    colstd = std(vx(:, i),1); 
+    colstd_sum = colstd_sum + colstd;
+end 
+colstd_mean = colstd_sum / cols1p;
+    
+li_d = (rowstd_mean + colstd_mean) / 2;
+
+
+% LD
+d = sqrt(vx.^2 + vy.^2);
+ld = mean(d(:));
+
+
+% MS-SSIM
+wt = [0.0448 0.2856 0.3001 0.2363 0.1333];
+ss = zeros(5, 1);
+for s = 1 : 5
+    ss(s) = ssim(x, y);
+    x = impyramid(x, 'reduce');
+    y = impyramid(y, 'reduce');
+end
+ms = wt * ss;
+
+
+% wv and wh
+rowstd_sum = 0;
+for i = 1:size(data, 1)
+    rowstd_top = std(vy(data(i,2), data(i,1):data(i,3)),1) / (data(i,3)-data(i,1)); 
+    rowstd_bot = std(vy(data(i,4), data(i,1):data(i,3)),1) / (data(i,3)-data(i,1)); 
+    rowstd_sum = rowstd_sum + rowstd_top + rowstd_bot;
+end
+wv = rowstd_sum / (2 * size(data, 1));
+
+colstd_sum = 0;
+for i = 1:size(data, 1)
+    colstd_left = std(vx(data(i,2):data(i,4), data(i,1)),1) / (data(i,4)- data(i,2)); 
+    colstd_right = std(vx(data(i,2):data(i,4), data(i,3)),1) / (data(i,4)- data(i,2)); 
+    colstd_sum = colstd_sum + colstd_left + colstd_right;
+end 
+wh = colstd_sum / (2 * size(data, 1));
+
+end