Added comments to LRMC and PCA

DEVANSH-DVJ · May 14, 2021 · bf6cd44 · bf6cd44
1 parent 656956c
commit bf6cd44
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diff --git a/algos/LRMC/LRMC.m b/algos/LRMC/LRMC.m
@@ -1,5 +1,20 @@
 function [recon, filtered] = LRMC(noisy, frameno, tau, kmax, tol, variant)
+    % Input:
+    %   noisy   : noisy video, [dim1 dim2 nframes] (uint8)
+    %   frameno : frame number to be denoised
+    %   tau     : step size
+    %   kmax    : maximum iterations
+    %   tol     : tolerance
+    %   variant : two binary digits representing
+    %               { whether second subset of missing pixels to be taken and
+    %                 replace in patchArr after every iteration }
+    % Output:
+    %   recon    : denoised video, [dim1 dim2 nframes] (uint8)
+    %   filtered : video after applying adaptive median filter, [dim1 dim2 nframes] (uint8)
+    % Brief:
+    %   Using Low Rank Matrix Completion to denoise video
 
+    % Switch case on variant
     switch variant
         case '00'
             sec_missing = false;
@@ -19,13 +34,16 @@
 
     [dim1, dim2, nframes] = size(noisy);
 
+    % Applying adaptive median filter
     filtered = zeros([dim1 dim2 nframes], 'uint8');
     for i=1:nframes
         filtered(:,:,i) = adapmedfilt(noisy(:,:,i), 11);
     end
 
+    % Finding out the first subset of missing pixels
     missing = (filtered ~= noisy);
 
+    % Generating patch array and missing array
     patchArr = zeros([64 (dim1/4 - 1) (dim2/4 - 1) nframes], 'uint8');
     missingArr = false(size(patchArr));
 
@@ -38,34 +56,41 @@
         end
     end
 
+    % Iteratively denoising each patch
     denoisedpatchArr = zeros(size(patchArr), 'double');
     for refj=1:(dim1/4 - 1)
         for refk=1:(dim2/4 - 1)
 
+            % Getting indices of matching patches
             indices = patchmatcher(patchArr, refj, refk, frameno);
 
+            % Creating patch matrix and Omega
             patchMat = zeros(64, size(indices, 2), 'uint8');
             patchOmega = false(64, size(indices, 2));
             for i=1:size(indices,2)
                 patchMat(:,i) = patchArr(:, indices(1,i), indices(2,i), indices(3,i));
                 patchOmega(:, i) = ~missingArr(:, indices(1,i), indices(2,i), indices(3,i));
             end
 
+            % Running our SVT algorithm
             [denoisedpatchMat, ~] = svti(cast(patchMat, 'double'), patchOmega, tau, kmax, tol, sec_missing);
 
+            % Finding the index of the reference patch in patch matrix
             for selfind=1+5*(frameno-1):5*frameno
                 if (indices(:, selfind) == [refj; refk; frameno;])
                     break;
                 end
             end
 
+            % Updating the reference patch
             if replace
                 patchArr(:, refj, refk, frameno) = denoisedpatchMat(:, selfind);
             end
             denoisedpatchArr(:, refj, refk, frameno) = denoisedpatchMat(:, selfind);
         end
     end
 
+    % Averaged reconstruction from denoised patch array
     recon = zeros(size(filtered), 'double');
     weight = recon;
     for i=1:nframes
@@ -77,5 +102,6 @@
         end
     end
     recon = recon ./ weight;
+    % Casting it to uint8 for consistency
     recon = cast(recon, 'uint8');
 end
diff --git a/algos/PCA/PCA.m b/algos/PCA/PCA.m
@@ -1,5 +1,16 @@
 function [recon, filtered] = PCA(noisy, frameno, C, variant)
+    % Input:
+    %   noisy   : noisy video, [dim1 dim2 nframes] (uint8)
+    %   frameno : frame number to be denoised
+    %   C       : reduced dimension for PCA
+    %   variant : one binary digit representing replace in patchArr after every iteration
+    % Output:
+    %   recon    : denoised video, [dim1 dim2 nframes] (uint8)
+    %   filtered : video after applying adaptive median filter, [dim1 dim2 nframes] (uint8)
+    % Brief:
+    %   Using Principal Component Analysis to denoise video
 
+    % Switch case on variant
     switch variant
         case '0'
             replace = false;
@@ -11,11 +22,13 @@
 
     [dim1, dim2, nframes] = size(noisy);
 
+    % Applying adaptive median filter
     filtered = zeros([dim1 dim2 nframes], 'uint8');
     for i=1:nframes
         filtered(:,:,i) = adapmedfilt(noisy(:,:,i), 11);
     end
 
+    % Generating patch array
     patchArr = zeros([64 (dim1/4 - 1) (dim2/4 - 1) nframes], 'uint8');
 
     for i=1:nframes
@@ -26,32 +39,39 @@
         end
     end
 
+    % Iteratively denoising each patch
     denoisedpatchArr = zeros(size(patchArr), 'double');
     for refj=1:(dim1/4 - 1)
         for refk=1:(dim2/4 - 1)
 
+            % Getting indices of matching patches
             indices = patchmatcher(patchArr, refj, refk, frameno);
 
+            % Creating patch matrix
             patchMat = zeros(64, size(indices, 2), 'uint8');
             for i=1:size(indices,2)
                 patchMat(:,i) = patchArr(:, indices(1,i), indices(2,i), indices(3,i));
             end
 
+            % Running our PCA algorithm
             [denoisedpatchMat] = pcai(cast(patchMat, 'double'), C);
 
+            % Finding the index of the reference patch in patch matrix
             for selfind=1+5*(frameno-1):5*frameno
                 if (indices(:, selfind) == [refj; refk; frameno;])
                     break;
                 end
             end
 
+            % Updating the reference patch
             if replace
                 patchArr(:, refj, refk, frameno) = denoisedpatchMat(:, selfind);
             end
             denoisedpatchArr(:, refj, refk, frameno) = denoisedpatchMat(:, selfind);
         end
     end
 
+    % Averaged reconstruction from denoised patch array
     recon = zeros(size(filtered), 'double');
     weight = recon;
     for i=1:nframes
@@ -63,5 +83,6 @@
         end
     end
     recon = recon ./ weight;
+    % Casting it to uint8 for consistency
     recon = cast(recon, 'uint8');
 end