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matcomp.m
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matcomp.m
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tic;
% Use K=50 for final run
K = 50;
[video,audio] = mmread('../carphone_qcif.y4m',1:K);
frames = video.frames;
f1=[];
for i=1:K
% color
% f1(:,:,(i-1)*3+1:(i-1)*3+3) = (frames(i).cdata);
% grayscale
f1(:,:,i) = rgb2gray(frames(i).cdata);
end
[H,W] = size(f1);
% Generate Noisy image
gauss = random('Normal',0,30,size(f1));
poiss = random('Poisson',40,size(f1));
noisy_im = f1 + gauss + poiss;
b=rand(size(f1));
c=imnoise(b,'salt & pepper',0.1);
ind0=find(c==0);
ind1=find(c==1);
noisy_im(ind0) = 0;
noisy_im(ind1) = 255;
% color
% figure,imshow(mat2gray(noisy_im(:,:,1:3)));
% grayscale
figure,imshow(mat2gray(noisy_im(:,:,1)));
% color
% im = noisy_im(:,:,1:3);
% grayscale
im = noisy_im(:,:,1);
% cat(1,im2col(im(:,:,1),[8 8]),im2col(im(:,:,2),[8 8]),im2col(im(:,:,3),[8 8]));
[m,n,p]=size(noisy_im);
for iter=1:size(noisy_im,3)
filt_im(:,:,iter) = medfilt2(noisy_im(:,:,iter));
end
new_im=zeros(size(im));
count=zeros(size(im));
% figure,imshow(mat2gray(filt_im(:,:,1:3)));
figure,imshow(mat2gray(filt_im(:,:,1)));
for i=1:4:m-8
disp(i);
for j=1:4:n-4
x2=min(i+7,m);
y2=min(j+7,n);
% ref_patch = filt_im(i:x2,j:y2,1:3);
ref_patch = filt_im(i:x2,j:y2,1);
% color
% P=zeros(8*8*3,250);
% grayscale
P=zeros(8*8,250);
a=ref_patch(:,:,1);
% b=ref_patch(:,:,2);
% c=ref_patch(:,:,3);
% vec_patch=cat(1,a(:),b(:),c(:));
vec_patch=a(:);
iter=1;
% Patch matching
% for k=1:3:p
for k=1:1:p
i2=min(i+11,m);
j2=min(j+11,n);
k2=min(k+2,p);
% temp = filt_im(i:i2,j:j2,k:k2);
temp = filt_im(i:i2,j:j2,k);
a = temp(:,:,1);
% b = temp(:,:,2);
% c = temp(:,:,3);
% patch=cat(1,im2col(a,[8 8]),im2col(b,[8 8]),im2col(c,[8 8]));
patch=im2col(a,[8 8]);
error = sum(abs(patch - vec_patch));
% Top 5 patches for frame k
[found,ind]=mink(error,5);
top_patch = patch(:,ind);
P(:,iter:iter+length(ind)-1) = top_patch;
iter=iter+length(ind);
end
% Select elements for set Omega
mu = mean(patch,2);
sd = std(patch,0,2);
% ind=[];
[omega] = find( patch>mu-2*sd & patch<mu+2*sd);
% length(ind)
P_omega1 = zeros(size(P));
P_omega1(omega) = P(omega);
% apply matrix completion
Qnew = zeros(size(P));
Qold = Qnew;
eps = 10^-5;
tou = 1;
% tou = rand(1)+1;
ratio = length(omega)/length(P_omega1);
sigmahat = mean( std(P_omega1,0,2) );
% color
% newmu = (sqrt(8*8*3) + sqrt(250) )*sqrt(ratio)*sigmahat;
% grayscale
newmu = (sqrt(8*8) + sqrt(250) )*sqrt(ratio)*sigmahat;
for z=1:30
temp=zeros(size(P_omega1));
temp2 = Qold - P_omega1;
temp(omega) = temp2(omega);
Rk = Qold - tou*temp2;
[U,D,V] = svd(Rk);
ind = find(D);
vals = max(diag(D)-tou*newmu,0);
D(ind) = vals;
Qnew = U*D*V';
err = Qnew-Qold;
err = sqrt( sum(err(:).^2) );
Qold = Qnew;
if err<eps
% disp('true');
break;
end
end
% Qnew(omega) = P_omega1(omega);
% restore first frame
error = sum(abs(Qnew - vec_patch));
[found,ind]=mink(error,1);
vec_res = Qnew(:,ind);
% vec_res = Qnew(:,1);
% color
% res = reshape(vec_res,8,8,3);
% new_im(i:x2,j:y2,1:3) = new_im(i:x2,j:y2,1:3) + res;
% count(i:x2,j:y2,1:3) = count(i:x2,j:y2,1:3) + 1;
% grayscale
res = reshape(vec_res,8,8);
% new_im(i:x2,j:y2,1) = res;
new_im(i:x2,j:y2,1) = new_im(i:x2,j:y2,1) + res;
count(i:x2,j:y2,1) = count(i:x2,j:y2,1) + 1;
end
end
%% reconstruction
% final_im=new_im./count;
final_im=new_im;
% color
% cropped_final = final_im(1:m-4,1:n,1:3);
% grayscale
cropped_final = final_im(1:m-4,1:n,1);
cropped_final = cropped_final(5:end-4,5:end-8);
% cropped_final = mat2gray(cropped_final)*255;
% color
% im = f1(:,:,1:3);
% cropped_im = im(1:m-4,1:n,1:3);
im = f1(:,:,1);
cropped_im = im(1:m-4,1:n,1);
cropped_im = cropped_im(5:end-4,5:end-8);
cropped_im = (cropped_im);
figure,imshow(mat2gray(cropped_final));
% color
% med_filt=mat2gray(filt_im(:,:,1:3));
% cropped_med = med_filt(1:m-4,1:n,1:3);
% grayscale
med_filt=(filt_im(:,:,1));
cropped_med = med_filt(1:m-4,1:n,1);
cropped_med = cropped_med(5:end-4,5:end-8);
psnrMed = psnr(uint8(mat2gray(cropped_med)*255),uint8(mat2gray(cropped_im)*255));
% rmse=norm( cropped_im(:)-cropped_med(:) )^2
% med1=10*log10( ( 255^2/norm( cropped_im(:)-cropped_med(:) )^2 ) );
disp('Median Filtering PSNR:');
disp(psnrMed);
% disp(med1);
% rmse = norm(cropped_im(:) - cropped_final(:))^2;
% psnrDenoised = 10*log10( (255^2)/rmse );
psnrDenoised = psnr(uint8(mat2gray(cropped_final)*255),uint8(mat2gray(cropped_im)*255));
% rmse=norm( cropped_im(:)-cropped_final(:) )^2
% med2=10*log10((255^2/norm(cropped_im(:)-cropped_final(:))^2));
disp('Matrix Completion PSNR:');
disp(psnrDenoised);
% disp(med2);
toc;