103 lines
2.7 KiB
Mathematica
103 lines
2.7 KiB
Mathematica
|
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
|