Mercurial > hg > smallbox
view toolboxes/AudioInpaintingToolbox/Solvers/inpaintFrame_OMP.m @ 138:56d719a5fd31 ivand_dev
Audio Inpaintin Toolbox
author | Ivan Damnjanovic lnx <ivan.damnjanovic@eecs.qmul.ac.uk> |
---|---|
date | Thu, 21 Jul 2011 14:27:47 +0100 |
parents | |
children |
line wrap: on
line source
function y = inpaintFrame_OMP(problemData,param) % Inpainting method based on OMP % % Usage: y = inpaintFrame_OMP(problemData,param) % % % Inputs: % - problemData.x: observed signal to be inpainted % - problemData.Imiss: Indices of clean samples % - param.D - the dictionary matrix (optional if param.D_fun is set) % - param.D_fun - a function handle that generates the dictionary % matrix param.D if param.D is not given. See, e.g., DCT_Dictionary.m and Gabor_Dictionary.m % - param.wa - Analysis window % % Outputs: % - y: estimated frame % % % ------------------- % % Audio Inpainting toolbox % Date: June 28, 2011 % By Valentin Emiya, Amir Adler, Michael Elad, Maria Jafari % This code is distributed under the terms of the GNU Public License version 3 (http://www.gnu.org/licenses/gpl.txt). % ======================================================== % To do next: use a faster implementation of OMP %% Load data and parameters x = problemData.x; IObs = find(~problemData.IMiss); p.N = length(x); E2 = param.OMPerr^2; E2M=E2*length(IObs); wa = param.wa(param.N); %% Build and normalized dictionary % build the dictionary matrix if only the dictionary generation function is given if ~isfield(param,'D') param.D = param.D_fun(param); end Dict=param.D(IObs,:); W=1./sqrt(diag(Dict'*Dict)); Dict=Dict*diag(W); xObs=x(IObs); %% OMP iterations residual=xObs; maxNumCoef = param.sparsityDegree; indx = []; currResNorm2 = E2M*2; % set a value above the threshold in order to have/force at least one loop executed j = 0; while currResNorm2>E2M && j < maxNumCoef, j = j+1; proj=Dict'*residual; [dum pos] = max(abs(proj)); indx(j)=pos; a=pinv(Dict(:,indx(1:j)))*xObs; residual=xObs-Dict(:,indx(1:j))*a; currResNorm2=sum(residual.^2); end %% Frame Reconstruction indx(length(a)+1:end) = []; Coeff = sparse(size(param.D,2),1); if (~isempty(indx)) Coeff(indx) = a; Coeff = W.*Coeff; end y = param.D*Coeff; return