Mercurial > hg > smallbox
view util/SMALL_denoise.m @ 156:a4d0977d4595 danieleb
First branch commit, danieleb
author | danieleb |
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date | Tue, 30 Aug 2011 11:12:31 +0100 |
parents | 8e660fd14774 |
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function solver=SMALL_denoise(Problem, solver) %% SMALL denoising - old replaced denoising function, would go out in v2.0 % % Function gets as input SMALL structure that contains SPARCO problem to % be solved, name of the toolbox and solver, and parameters file for % particular solver. % It is based on omp/omps denoising by Ron Rubenstein (KSVD toolbox) % % Outputs are denoised image, psnr, number of non-zero coeficients and % time spent % % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2009 Ivan Damnjanovic. % % 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 2 of the % License, or (at your option) any later version. See the file % COPYING included with this distribution for more information. % %% %%%%% denoise the signal %%%%% fprintf('\nStarting %s... \n', solver.name); start=cputime; %% if strcmpi(solver.toolbox,'ompbox') if (~isfield(solver.param,'lambda')) solver.param.lambda = Problem.maxval/(10*Problem.sigma); end solver.param = Problem; %solver.param.memusage = 'high'; solver.param = rmfield(solver.param, {'Noisy' 'Original' 'b' 'm' 'n' 'p' 'initdict'}); solver.param.x = Problem.Noisy; solver.param.dict = Problem.A; p = Problem.signalDim; msgdelta=5; % call the appropriate ompdenoise function if (p==1) [y,nz] = ompdenoise1(solver.param,msgdelta); elseif (p==2) [y,nz] = ompdenoise2(solver.param,msgdelta); elseif (p==3) [y,nz] = ompdenoise3(solver.param,msgdelta); else [y,nz] = ompdenoise(solver.param,msgdelta); end elseif strcmpi(solver.toolbox,'ompsbox') if (~isfield(solver.param,'lambda')) solver.param.lambda = Problem.maxval/(10*Problem.sigma); end solver.param = Problem; %solver.param.memusage = 'high'; solver.param = rmfield(solver.param, {'Noisy' 'Original' 'b' 'm' 'n' 'p' 'initdict'}); solver.param.x = Problem.Noisy; if issparse(Problem.A) solver.param.A = Problem.A; else solver.param.A = sparse(Problem.A); end p = Problem.signalDim; msgdelta=5; % call the appropriate ompdenoise function if (p==1) [y,nz] = ompsdenoise1(solver.param,msgdelta); elseif (p==2) [y,nz] = ompsdenoise2(solver.param,msgdelta); elseif (p==3) [y,nz] = ompsdenoise3(solver.param,msgdelta); else [y,nz] = ompsdenoise(solver.param,msgdelta); end else printf('\nToolbox has not been registered. Please change SMALL_learn file.\n'); return end %% solver.time = cputime - start; fprintf('\n%s finished task in %2f seconds. \n', solver.name, solver.time); solver.reconstructed.Image=y; solver.reconstructed.psnr=20*log10(Problem.maxval * sqrt(numel(Problem.Original)) / norm(Problem.Original(:)-solver.reconstructed.Image(:))); solver.reconstructed.nz=nz; end