Soft_B_denois_steerable

PURPOSE ^

It 'cleans' an image using bayesian formulation with laplacian prior

SYNOPSIS ^

function [IMM,S,P]=Soft_B_denois_steerable(Im2,Var)

DESCRIPTION ^

 It 'cleans' an image using bayesian formulation with laplacian prior
 as explained in Simoncelli '99.
 "Bayesian denoising of visual images in the wavelet domain."

 USE: [IMM,S,P]=Soft_B_denois_steerable(Im2,Var)

 INPUTS
 Im2 = noisy image;
 Var = noise variance

 OUTPUT
 IMM es la imagen limpiada;
 S and P are the estimated values that define the prior distributions
 for each wavelet scale

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 % It 'cleans' an image using bayesian formulation with laplacian prior
0002 % as explained in Simoncelli '99.
0003 % "Bayesian denoising of visual images in the wavelet domain."
0004 %
0005 % USE: [IMM,S,P]=Soft_B_denois_steerable(Im2,Var)
0006 %
0007 % INPUTS
0008 % Im2 = noisy image;
0009 % Var = noise variance
0010 %
0011 % OUTPUT
0012 % IMM es la imagen limpiada;
0013 % S and P are the estimated values that define the prior distributions
0014 % for each wavelet scale
0015 
0016 function [IMM,S,P]=Soft_B_denois_steerable(Im2,Var)
0017 ImDeg = Im2;
0018 desviacion=sqrt(Var);
0019 
0020 NW=4;
0021 [pyrDeg,indDeg]=buildsfpyr(double(ImDeg),4,7);
0022 pyrEst=zeros(size(pyrDeg));
0023 
0024 load indices_epsilon_4esc_8ori
0025 indices=indices*sqrt(Var)/sqrt(400);
0026 for escala=1:NW
0027     for ori=1:8
0028         [escala ori]
0029 
0030         PP=[];
0031         A=[];
0032         for i=1:2^(9-escala)
0033             for j=1:2^(9-escala)
0034                 pp=pos_s_pyr([escala ori i j],indDeg);
0035                 A(i,j)=pyrDeg(pp);
0036                 PP(i,j)=pp;
0037             end
0038         end
0039         subbanda=A;
0040 
0041         QQ=1;
0042         MM=zeros(3,15*15);
0043         for s=logspace(-1.5,2.5,25)
0044             for p=linspace(0.35,2,20)
0045                 l=likelihooddegradedaB([s p],subbanda,indices(PP(1,1))^2);
0046                 MM(1,QQ)=s;
0047                 MM(2,QQ)=p;
0048                 MM(3,QQ)=l;
0049                 QQ=QQ+1;
0050             end
0051         end
0052         [MN,IN]=min(MM(3,:));
0053         S(escala,ori)=MM(1,IN);
0054         P(escala,ori)=MM(2,IN);
0055 
0056         pyrsubDeg = subbanda(:);
0057         lookup = [];
0058 
0059         pasox=0.5;
0060         rango = [round(min(pyrsubDeg))-30:pasox:round(max(pyrsubDeg))+30];
0061         num2=[];den2=[];
0062         for valor=rango
0063             num = sum(pasox.*blsnum(rango,valor,S(escala,ori),P(escala,ori),desviacion));
0064             denom = sum(pasox.*blsdenom(rango,valor,S(escala,ori),P(escala,ori),desviacion));
0065             num2=[num2 num];
0066             den2=[den2 denom];
0067             lookup = [lookup num/denom];
0068         end
0069 
0070         estimado=zeros(size(pyrsubDeg));
0071         for indice=1:length(pyrsubDeg)
0072             estimado(indice) = interp1(rango,lookup,pyrsubDeg(indice));
0073         end
0074         BB=reshape(estimado,2^(9-escala),2^(9-escala));
0075         pyrEst(PP)=BB;
0076     end
0077 end
0078 pyrEst(length(pyrDeg)-32*32+1:length(pyrDeg)) = pyrDeg(length(pyrDeg)-32*32+1:length(pyrDeg));
0079 IMM=reconsfpyr(pyrEst,indDeg);

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