Mercurial > hg > smallbox
view toolboxes/AudioInpaintingToolbox/Solvers/inpaintFrame_consOMP.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_consOMP(problemData,param) % Inpainting method based on OMP with a constraint % on the amplitude of the reconstructed samples an optional constraint % on the maximum value of the clipped samples % % Usage: y = inpaintFrame_consOMP(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 % - param.Upper_Limit - if present and non-empty this fiels % indicates that an upper limit constraint is active and its % integer value is such that % % Outputs: % - y: estimated frame % % Note that the CVX library is needed. % % ------------------- % % 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). % ======================================================== %% 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 the dictionary matrix if only the dictionary generation function is given if ~isfield(param,'D') param.D = param.D_fun(param); end % clipping level detection clippingLevelEst = max(abs(x(:)./wa(:))); IMiss = true(length(x),1); IMiss(IObs) = false; IMissPos = find(x>=0 & IMiss); IMissNeg = find(x<0 & IMiss); DictPos=param.D(IMissPos,:); DictNeg=param.D(IMissNeg,:); % Clipping level: take the analysis window into account wa_pos = wa(IMissPos); wa_neg = wa(IMissNeg); b_ineq_pos = wa_pos(:)*clippingLevelEst; b_ineq_neg = -wa_neg(:)*clippingLevelEst; if isfield(param,'Upper_Limit') && ~isempty(param.Upper_Limit) b_ineq_pos_upper_limit = wa_pos(:)*param.Upper_Limit*clippingLevelEst; b_ineq_neg_upper_limit = -wa_neg(:)*param.Upper_Limit*clippingLevelEst; else b_ineq_pos_upper_limit = Inf; b_ineq_neg_upper_limit = -Inf; end %% Dict=param.D(IObs,:); W=1./sqrt(diag(Dict'*Dict)); Dict=Dict*diag(W); xObs=x(IObs); 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; if isinf(b_ineq_pos_upper_limit) %% CVX code cvx_begin cvx_quiet(true) variable a(j) %minimize( sum(square(xObs-Dict*a))) minimize(norm(Dict(:,indx)*a-xObs)) subject to DictPos(:,indx)*(W(indx).*a) >= b_ineq_pos DictNeg(:,indx)*(W(indx).*a) <= b_ineq_neg cvx_end if cvx_optval>1e3 cvx_begin cvx_quiet(true) variable a(j) minimize(norm(Dict(:,indx)*a-xObs)) cvx_end end else %% CVX code cvx_begin cvx_quiet(true) variable a(j) %minimize( sum(square(xObs-Dict*a))) minimize(norm(Dict(:,indx)*a-xObs)) subject to DictPos(:,indx)*(W(indx).*a) >= b_ineq_pos DictNeg(:,indx)*(W(indx).*a) <= b_ineq_neg DictPos(:,indx)*(W(indx).*a) <= b_ineq_pos_upper_limit DictNeg(:,indx)*(W(indx).*a) >= b_ineq_neg_upper_limit cvx_end if cvx_optval>1e3 cvx_begin cvx_quiet(true) variable a(j) minimize(norm(Dict(:,indx)*a-xObs)) cvx_end end 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