Poisson Non-Local Sparse PCA

Simple example on Ridge

We present here the NLSPCA algorithm to denoise Poisson corrupted images.

Authors: J. Salmon, Z. Harmany, C.-A. Deledalle, R. Willett. PDF
Corresponding Matlab toolbox :
-2D images NLSPCA_code.ZIP
-hyperspectral images NLPCA_hyperspectral_code.ZIP (fast v2: NLPCA_hyperspectral_fast_code.ZIP thanks to Anthony Wang and Albert Oh).
Copyright (C) 2012 NL-PCA project. See The GNU Public License (GPL)

Initialization

clear all
close all

addpath('functions')
addpath('tools')

Loading 'Ridge' image

ima_ori=double(imread('./data/Ridges.png'));

Noisy image generation

peak=0.1;
sd=2;
rng(sd)
Q = max(max(ima_ori)) /peak;
ima_lambda = ima_ori / Q;
ima_lambda(ima_lambda == 0) = min(min(ima_lambda(ima_lambda > 0)));
ima_nse_poiss = knuth_poissrnd(ima_lambda);
[m,n]=size(ima_nse_poiss);

Parameters:numbers

param.Patch_width=20;
param.nb_axis=4;
param.nb_clusters=14;
param.bandwith_smooth=2;
param.sub_factor=2;
param.big_cluster1=1;% special case for the biggest cluster 1st pass
param.big_cluster2=1;% special case for the biggest cluster 2nd pass
param.double_iteration=0;%1 or 2 pass of the whole algorithm
param.eps_stop=5e-3; %loop stoping criterion
param.epsilon_cond=1e-4;
param.nb_iterations=15;%number of iteration (Gordon)
param.cste=70;
param.func_tau=@(X) lasso_tau(X{1},X{2},param.cste);
param.bin=1; % subsampling factor, 0 is without it

Denoising with NLSPCA

tic
ima_fil=NLSPCA(ima_nse_poiss,ima_nse_poiss,param);
toc

Elapsed time is 75.562460 seconds.

Result display

figure('Position',[100 100   1200 600])
ax(1) = subplot(1, 3, 1);
plotimage(Q * ima_nse_poiss);
title(sprintf('Noisy PSNR = %f',psnr(Q*ima_nse_poiss, Q*ima_lambda, 255)));
ax(2) = subplot(1, 3, 2);
plotimage(Q * ima_lambda);
title('Original');
ax(3) = subplot(1, 3, 3);
plotimage(Q * ima_fil);
title(sprintf('NLSPCA, PSNR = %f',psnr(Q*ima_fil, Q*ima_lambda, 255)));
linkaxes(ax);

Copyright ® 2011