% Demonstration of image restoration on USPS database.%% Linear PCA and Kernel PCA models are learnt from files.%% Modification:% 5-may-2004, VF% 22-apr-2004, VF% == input files ======================kpca_filename = 'GreedyKpcaMoldeUsps5.mat';  % kpca modellpca_filename = 'LinearPCAModelUSPS.mat';       % linear PCA modelinput_data_file = '/home.dokt/xfrancv/data/usps_noisy';  % USPS % loading ...load(kpca_filename,'kpca_model');load(lpca_filename,'lpca_model');load(input_data_file);% get indices of examples to denoiseinx = [];for i=1:10,  tmp = find(tst.y == i);  inx = [inx, tmp(1) ];end% get noisy and ground truth numeralsnoisy_X = tst.X(:,inx);  gnd_X = tst.gnd_X(:,inx);% Kernel PCA denoisingkpca_X = kpimage( noisy_X, kpca_model);lpca_X = lpimage( noisy_X, lpca_model);% display resultsh=figure; set(h,'name','Denoised by greedy KPCA');showim( kpca_X);h=figure; set(h,'name','Denoised by linear PCA');showim( lpca_X);h=figure; set(h,'name','Ground truth');showim( gnd_X);h=figure; set(h,'name','Noisy');showim( noisy_X);