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%-------------------------------------------------------------------------- % 3D Shearlet-MATLAB %-------------------------------------------------------------------------- % Pooran Singh Negi and Demetrio Labate %-------------------------------------------------------------------------- % Video Denoising Demo % created: 08-12-11 % modified 4-20-13 %--------------------------------------------------------------------------

%-------------------------------------------------------------------------- % Sample Script showing the use of shearlet 3d functions for video denosing % In this script we show the the usage of 3D shearlet for removing additive % white Gaussian noise added to video. This is done via Hard thresholding % but significant improvement can be acheived by using better denoising techniques % we used sequence tempete, mobile2_sequence and coastguard_sequence in our paper % Pooran SinghNegi, Demetrio Labate. 3D Discrete Shearlet Transform and Video Processing , % IEEE Trans. Image Processing, vol. 21, no. 6, pp. 2944-2954, June 2012. %--------------------------------------------------------------------------

tic; %matlabpool open 3 addpath('C:/Users/rubab/Downloads/3Dshearlet_toolbox/CONVNFFT_Folder'); addpath('C:/Users/rubab/Downloads/3Dshearlet_toolbox/3DBP'); addpath('C:/Users/rubab/Downloads/3Dshearlet_toolbox/3DShearTrans'); addpath('C:/Users/rubab/Downloads/3Dshearlet_toolbox./Util'); addpath('C:/Users/rubab/Downloads/3Dshearlet_toolbox/Data'); %--------------------------------------------------------------------------

%current implementation is highly redendent so it running in low memory system % choose single to save some space %--------------------------------------------------------------------------

dataClass='single';% 'single' or 'double' %Thresholding multiplier for hardthresholding %T=ones(level+1,1)*3.08; T=[ 3.3 3.0 3.0 3.0]; filterDilationType='422';%%only two type '422' or '442'. currently only 422 supported filterType='meyer';%%only meyer type implemented %--------------------------------------------------------------------------

sigma=30; % standard deviation level=1; % choose level of decomposition , %--------------------------------------------------------------------------

%-------------------------------------------------------------------------- %As per the level choosen different number of directinal band wil be used. %if level =1, then there will be 8X8 band in each of 3 pyramidal zone for finest level 1 %if level=2, finest level 1 will have 8X8 band and next coarser level 2 %will have 4X4 in each of the 3 pyramidal zone %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%NOTE::THESE ARE JUST SUGGESTIVE DIRECTIONAL BAND COMPOSITION %% USER MAY TRY TO PLAY WITH THEM %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Cell to specify different directional band at as per level choosen %In current implementation specifying second number of direction is %ignored %--------------------------------------------------------------------------

dBand={{[ 8 8]}, ... %%%%for level =1 {[8 8 ],[6 6]}, ... %%%% for level =2 {[8 8 ], [6 6],[4 4]}, ... %%%% for level =3 {[8 8],[8 8],[4 4],[4 4]}}; %%%%% for level =4 %--------------------------------------------------------------------------

%Load Data and introduce some noise for simulation %load tempete % load mobile2_sequence load coastguard_sequence

%choose filter size such that remainder is 0 when divided by number of %direction in that level

filterSize=[24 24 24 24];

x=double(X); xn = x + sigma * randn(size(x)); fprintf('introduced PSNR %f\n',PSNR(x,xn));

disp('Processing ...'); %Build Windowing Filter for different Band F=GetFilter(filterType,level,dBand,filterSize,filterDilationType ,'single');

%Do the Band Pass of noisy Data BP=DoPyrDec(xn,level);

%for storing partial reconstructed bandpass data partialBP=cell(size(BP)); recBP=cell(size(BP));

%-------------------------------------------------------------------------- % Determines via Monte Carlo the standard deviation of % the white Gaussian noise with for each scale and % directional components when a white Gaussian noise of % standard deviation of 1 is feed through. %--------------------------------------------------------------------------

nsstScalarFileName=['nsstScalarsData' regexprep( num2str( [level dBand{level}{:}]) ,'[^\w'']','') '.mat']; if exist(nsstScalarFileName,'file') load(nsstScalarFileName); else nsstScalars = NsstScalars(size(x),F,dataClass); save(nsstScalarFileName, 'nsstScalars'); end

%-------------------------------------------------------------------------- %Compute Shearlet Coefficient, Threshold and denoise. %If large memory is available then can collect all the %respective pyramidal cone data in a 1X3 cell and can do %further processing in a single function %--------------------------------------------------------------------------

for pyrCone=1:3 shCoeff=ShDec(pyrCone,F,BP,level,dataClass); %clear F{pyrCone,:} shCoeff=ThresholdShCoeff(shCoeff,nsstScalars,pyrCone,sigma,T); partialBP{pyrCone}=ShRec(shCoeff); %clear shCoeff; end clear F; clear shCoeff; %-------------------------------------------------------------------------- %%%%%%%%%%%%%%Sum Up different pyramidal cone Band Pass Data%%%%%%%%%%%% %--------------------------------------------------------------------------

for l=1:level %% Assuming different pyramidal zone have same shCoeff size at different %%level recBP{l}=zeros(size(partialBP{1}{l}),dataClass); for pyrCone =1:3 recBP{l}=recBP{l}+ partialBP{pyrCone}{l}; end end

recBP{level+1}=BP{level+1};

% Do Reconstruction xRec=DoPyrRec(recBP);

fprintf('PSNR %f\n', PSNR(x,xRec));

disp('Done!');
%matlabpool close
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