bmailhe@244: %% Demostration of the use of the UNLocBox solver to solve an image denoising problem of the SMALLBox form
bmailhe@244: %
bmailhe@244: % argmin_x ||x||_1 such that ||Ax-b||_2 < sima_t
bmailhe@244: %
bmailhe@244: %   The dictionary A is first learn using SMALLbox
bmailhe@244: %
bmailhe@244: % Queen Mary University
bmailhe@244: % 29 August 2012
bmailhe@244: % Nathanael Perraudin
bmailhe@244: % nathanael.perraudin@epfl.ch
bmailhe@244: 
bmailhe@244: 
bmailhe@244: % % Initialisation
bmailhe@244: % clear all;
bmailhe@244: % close all;
bmailhe@244: % clc;
bmailhe@244: 
bmailhe@244: 
bmailhe@244: %% Load an image and create the problem
bmailhe@244: 
bmailhe@244: % Defining Image Denoising Problem as Dictionary Learning
bmailhe@244: % Problem. As an input we set the number of training patches.
bmailhe@244: 
bmailhe@244: SMALL.Problem = generateImageDenoiseProblem('', 40000);
bmailhe@244: 
bmailhe@244: 
bmailhe@244: %%
bmailhe@244: %   Use KSVD Dictionary Learning Algorithm to Learn overcomplete dictionary
bmailhe@244: 
bmailhe@244: %   Initialising Dictionary structure
bmailhe@244: %   Setting Dictionary structure fields (toolbox, name, param, D and time)
bmailhe@244: %   to zero values
bmailhe@244: 
bmailhe@244: SMALL.DL=SMALL_init_DL();
bmailhe@244: 
bmailhe@244: % Defining the parameters needed for dictionary learning
bmailhe@244: 
bmailhe@244: SMALL.DL.toolbox = 'KSVD';
bmailhe@244: SMALL.DL.name = 'ksvd';
bmailhe@244: 
bmailhe@244: %   Defining the parameters for KSVD
bmailhe@244: %   In this example we are learning 256 atoms in 20 iterations, so that
bmailhe@244: %   every patch in the training set can be represented with target error in
bmailhe@244: %   L2-norm (EData)
bmailhe@244: %   Type help ksvd in MATLAB prompt for more options.
bmailhe@244: 
bmailhe@244: Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
bmailhe@244: maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
bmailhe@244: 
bmailhe@244: SMALL.DL.param=struct(...
bmailhe@244:     'Edata', Edata,...
bmailhe@244:     'initdict', SMALL.Problem.initdict,...
bmailhe@244:     'dictsize', SMALL.Problem.p,...
bmailhe@244:     'iternum', 20,...
bmailhe@244:     'memusage', 'high');
bmailhe@244: 
bmailhe@244: %   Learn the dictionary
bmailhe@244: 
bmailhe@244: SMALL.DL = SMALL_learn(SMALL.Problem, SMALL.DL(1));
bmailhe@244: 
bmailhe@244: %   Set SMALL.Problem.A dictionary
bmailhe@244: %   (backward compatiblity with SPARCO: solver structure communicate
bmailhe@244: %   only with Problem structure, ie no direct communication between DL and
bmailhe@244: %   solver structures)
bmailhe@244: 
bmailhe@244: SMALL.Problem.A = SMALL.DL.D;
bmailhe@244: SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
bmailhe@244: 
bmailhe@244: %% Solving the problem with UNLocBox
bmailhe@244: % This might not be the better way to solve the problem...
bmailhe@244: 
bmailhe@244: 
bmailhe@244: % Set the different parameter
bmailhe@244: 
bmailhe@244: SMALL.solver=SMALL_init_solver; % Initialisation
bmailhe@244: SMALL.solver.toolbox='UNLocBox';     % select the UNLocBox solver
bmailhe@244: SMALL.solver.name='Douglas_Rachford'; % 'Forward_Backard' 'ADMM' Warning forward backward still need some review...
bmailhe@244: SMALL.solver.param.sigma=1.15*sqrt(SMALL.Problem.m*SMALL.Problem.n)*SMALL.Problem.sigma; % set the radius of the ball
bmailhe@244: SMALL.solver.param.max_iter=100;
bmailhe@244: 
bmailhe@244: 
bmailhe@244: SMALL.solver=SMALL_solve(SMALL.Problem, SMALL.solver);
bmailhe@244: 
bmailhe@244: 
bmailhe@244: SMALL_ImgDeNoiseResult(SMALL);