demo.m

I developed an algorithm for using local ICA in denoising multidimensional data. It uses delay embed

% demonstrates denoise package

load lena.mat

figure
colormap(gray)

noise=randn(size(lena))*100;
fprintf('Original Image and noisy image\n<hit return>\n');
imagesc([lena,lena+noise]);
pause;

lica_def_denoised=denoise(lena+noise,'verbose',2);
fprintf('Local ica denoised (default parameters)\n<hit return>\n');
imagesc([lena+noise,lica_def_denoised]);
pause;

lica_fastica_denoised=denoise(lena+noise,'method','fastica','verbose',2);
fprintf('Local ica denoised (fastica)\n<hit return>\n');
imagesc([lena+noise,lica_fastica_denoised]);
pause;

lpca_denoised=denoise(lena+noise,'method','pca','verbose',2);
fprintf('Local pca denoised\n<hit return>\n');
imagesc([lena+noise,lpca_denoised]);
pause;

fprintf('SNR:                      %f\n',SNR(lena,lena+noise))
fprintf('SNR (Local ica, default)  %f\n',SNR(lena,lica_def_denoised))
fprintf('SNR (Local ica, fastica)  %f\n',SNR(lena,lica_fastica_denoised))
fprintf('SNR (Local pca):          %f\n',SNR(lena,lpca_denoised))

pause;

[opt_lica_denoised,mdl_criterion]=denoise(lena+noise,'delaydim',[35:5:50],'cluster',[20:10:60],'verbose',1);
surf(mdl_criterion)
fprintf('SNR (Local ica, optimal)  %f\n',SNR(lena,opt_lica_denoised))