Mercurial > hg > smallbox
comparison toolboxes/AudioInpaintingToolbox/Solvers/inpaintFrame_consOMP.m @ 144:19e0af570914 release_1.5
Merge from branch "ivand_dev"
author | Ivan <ivan.damnjanovic@eecs.qmul.ac.uk> |
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date | Tue, 26 Jul 2011 15:14:15 +0100 |
parents | 56d719a5fd31 |
children |
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143:8d866d96f006 | 144:19e0af570914 |
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1 function y = inpaintFrame_consOMP(problemData,param) | |
2 % Inpainting method based on OMP with a constraint | |
3 % on the amplitude of the reconstructed samples an optional constraint | |
4 % on the maximum value of the clipped samples | |
5 % | |
6 % Usage: y = inpaintFrame_consOMP(problemData,param) | |
7 % | |
8 % | |
9 % Inputs: | |
10 % - problemData.x: observed signal to be inpainted | |
11 % - problemData.Imiss: Indices of clean samples | |
12 % - param.D - the dictionary matrix (optional if param.D_fun is set) | |
13 % - param.D_fun - a function handle that generates the dictionary | |
14 % matrix param.D if param.D is not given. See, e.g., DCT_Dictionary.m and Gabor_Dictionary.m | |
15 % - param.wa - Analysis window | |
16 % - param.Upper_Limit - if present and non-empty this fiels | |
17 % indicates that an upper limit constraint is active and its | |
18 % integer value is such that | |
19 % | |
20 % Outputs: | |
21 % - y: estimated frame | |
22 % | |
23 % Note that the CVX library is needed. | |
24 % | |
25 % ------------------- | |
26 % | |
27 % Audio Inpainting toolbox | |
28 % Date: June 28, 2011 | |
29 % By Valentin Emiya, Amir Adler, Michael Elad, Maria Jafari | |
30 % This code is distributed under the terms of the GNU Public License version 3 (http://www.gnu.org/licenses/gpl.txt). | |
31 % ======================================================== | |
32 | |
33 %% Load data and parameters | |
34 | |
35 x = problemData.x; | |
36 IObs = find(~problemData.IMiss); | |
37 p.N = length(x); | |
38 E2 = param.OMPerr^2; | |
39 E2M=E2*length(IObs); | |
40 wa = param.wa(param.N); | |
41 | |
42 % build the dictionary matrix if only the dictionary generation function is given | |
43 if ~isfield(param,'D') | |
44 param.D = param.D_fun(param); | |
45 end | |
46 | |
47 | |
48 % clipping level detection | |
49 clippingLevelEst = max(abs(x(:)./wa(:))); | |
50 | |
51 IMiss = true(length(x),1); | |
52 IMiss(IObs) = false; | |
53 IMissPos = find(x>=0 & IMiss); | |
54 IMissNeg = find(x<0 & IMiss); | |
55 | |
56 DictPos=param.D(IMissPos,:); | |
57 DictNeg=param.D(IMissNeg,:); | |
58 | |
59 % Clipping level: take the analysis window into account | |
60 wa_pos = wa(IMissPos); | |
61 wa_neg = wa(IMissNeg); | |
62 b_ineq_pos = wa_pos(:)*clippingLevelEst; | |
63 b_ineq_neg = -wa_neg(:)*clippingLevelEst; | |
64 if isfield(param,'Upper_Limit') && ~isempty(param.Upper_Limit) | |
65 b_ineq_pos_upper_limit = wa_pos(:)*param.Upper_Limit*clippingLevelEst; | |
66 b_ineq_neg_upper_limit = -wa_neg(:)*param.Upper_Limit*clippingLevelEst; | |
67 else | |
68 b_ineq_pos_upper_limit = Inf; | |
69 b_ineq_neg_upper_limit = -Inf; | |
70 end | |
71 | |
72 %% | |
73 Dict=param.D(IObs,:); | |
74 W=1./sqrt(diag(Dict'*Dict)); | |
75 Dict=Dict*diag(W); | |
76 xObs=x(IObs); | |
77 | |
78 residual=xObs; | |
79 maxNumCoef = param.sparsityDegree; | |
80 indx = []; | |
81 currResNorm2 = E2M*2; % set a value above the threshold in order to have/force at least one loop executed | |
82 j = 0; | |
83 while currResNorm2>E2M && j < maxNumCoef, | |
84 j = j+1; | |
85 proj=Dict'*residual; | |
86 [dum pos] = max(abs(proj)); | |
87 indx(j)=pos; | |
88 a=pinv(Dict(:,indx(1:j)))*xObs; | |
89 residual=xObs-Dict(:,indx(1:j))*a; | |
90 currResNorm2=sum(residual.^2); | |
91 end; | |
92 | |
93 | |
94 if isinf(b_ineq_pos_upper_limit) | |
95 %% CVX code | |
96 cvx_begin | |
97 cvx_quiet(true) | |
98 variable a(j) | |
99 %minimize( sum(square(xObs-Dict*a))) | |
100 minimize(norm(Dict(:,indx)*a-xObs)) | |
101 subject to | |
102 DictPos(:,indx)*(W(indx).*a) >= b_ineq_pos | |
103 DictNeg(:,indx)*(W(indx).*a) <= b_ineq_neg | |
104 cvx_end | |
105 if cvx_optval>1e3 | |
106 cvx_begin | |
107 cvx_quiet(true) | |
108 variable a(j) | |
109 minimize(norm(Dict(:,indx)*a-xObs)) | |
110 cvx_end | |
111 end | |
112 else | |
113 %% CVX code | |
114 cvx_begin | |
115 cvx_quiet(true) | |
116 variable a(j) | |
117 %minimize( sum(square(xObs-Dict*a))) | |
118 minimize(norm(Dict(:,indx)*a-xObs)) | |
119 subject to | |
120 DictPos(:,indx)*(W(indx).*a) >= b_ineq_pos | |
121 DictNeg(:,indx)*(W(indx).*a) <= b_ineq_neg | |
122 DictPos(:,indx)*(W(indx).*a) <= b_ineq_pos_upper_limit | |
123 DictNeg(:,indx)*(W(indx).*a) >= b_ineq_neg_upper_limit | |
124 cvx_end | |
125 if cvx_optval>1e3 | |
126 cvx_begin | |
127 cvx_quiet(true) | |
128 variable a(j) | |
129 minimize(norm(Dict(:,indx)*a-xObs)) | |
130 cvx_end | |
131 end | |
132 end | |
133 | |
134 %% Frame Reconstruction | |
135 indx(length(a)+1:end) = []; | |
136 | |
137 Coeff = sparse(size(param.D,2),1); | |
138 if (~isempty(indx)) | |
139 Coeff(indx) = a; | |
140 Coeff = W.*Coeff; | |
141 end | |
142 y = param.D*Coeff; | |
143 | |
144 return |