DEMO_SDMM - Example of use of the sdmm solver
Program code:
%DEMO_SDMM Example of use of the sdmm solver
%
% We present an example of the solver through an image denoising problem.
% We express the problem cas
%
% argmin_x,y,z ||x-b||_2^2 + tau1*||y||_TV + tau2 * ||H(z)||_1 such that x = y = Hz
%
% Where b is the degraded image, tau_1 and tau_2 two real positive constant and H a linear operator on x.
% H is a wavelet operator. We set:
%
% g_1(x)=||x||_{TV}
% We define the prox of g_1 as:
%
% prox_{f1,gamma} (z) = argmin_{x} 1/2 ||x-z||_2^2 + gamma ||z||_TV
%
% g_2(x)=||H(x)||_1
% We define the prox of g_2 as:
%
% prox_{f1,gamma} (z) = argmin_{x} 1/2 ||x-z||_2^2 + gamma ||H(z)||_1
%
% f(x)=||x-b||_2^2
% We define the gradient as:
%
% grad_f(x) = 2 * (x-b)
%
% Results
% -------
%
% Figure 1: Original image
%
% This figure shows the original image (The cameraman).
%
% Figure 2: Depleted image
%
% This figure shows the image after addition of the noise
%
% Figure 3: Reconstruted image
%
% This figure shows the reconstructed image thanks to the algorithm.
%
% The rwt toolbox is needed to run this demo.
%
% References:
% P. Combettes and J. Pesquet. Proximal splitting methods in signal
% processing. Fixed-Point Algorithms for Inverse Problems in Science and
% Engineering, pages 185--212, 2011.
%
%
% Url: https://epfl-lts2.github.io/unlocbox-html/doc/demos/demo_sdmm.html
% Copyright (C) 2012-2016 Nathanael Perraudin.
% This file is part of UNLOCBOX version 1.7.4
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% Author: Nathanael Perraudin
% Date: November 2012
%% Initialisation
clear;
close all;
% Loading toolbox
init_unlocbox();
verbose = 2; % verbosity level
%% Defining the problem
% Original image
im_original = lena();
% Depleted image
sigma=0.9;
b=im_original+sigma^2*rand(size(im_original));
%% Defining proximal operators
% setting different parameter for the simulation
tau1 = 0.2; % regularization parameter for the TV norm
tau2 = 0.2; % regularization parameter for the wavelet
% setting the function f1
% ----- TV norm ------
param_tv.verbose = verbose - 1;
param_tv.maxit = 100;
g_tv.prox = @(x, T) prox_tv(x, T*tau1, param_tv);
g_tv.eval = @(x) tau1 * norm_tv(x);
g_tv.x0 = b;
g_tv.L = @(x) x;
g_tv.Lt = @(x) x;
% ----- Wavelet ------
L=8;
A2 = @(x) fwt2(x,'db1',L);
A2t = @(x) ifwt2(x,'db1',L);
param_l1.verbose = verbose - 1;
g_l1.prox = @(x, T) prox_l1(x, T*tau2, param_l1);
g_l1.eval = @(x) tau2*norm(reshape(A2(x),[],1),1);
g_l1.L = A2;
g_l1.Lt = A2t;
g_l1.x0 = b;
% ----- L2 norm ------
paraml2.verbose = verbose-1;
paraml2.y = b;
g_l2.prox=@(x, T) prox_l2(x,T,paraml2);
g_l2.eval=@(x) norm(x,2);
g_l2.x0=b;
g_l2.L=@(x) x;
g_l2.Lt=@(x) x;
%% Solving the problem
% Parameter for the sum of function: F
F={g_l1,g_tv, g_l2};
param_solver.Qinv = @(x) 1/3*x;
param_solver.maxit=30;
param_solver.verbose = verbose;
% To see the image during the reconstruction
% fig = figure(100);
% param_solver.do_sol = @(x) plot_image(x,fig);
% solving the problem
sol=sdmm(F,param_solver);
% close(100);
%% displaying the result
imagesc_gray(im_original, 1, 'Original image');
imagesc_gray(b, 2, 'Depleted image');
imagesc_gray(sol, 3, 'Reconstructed image');
%% Closing the toolbox
close_unlocbox();