restore_p

PURPOSE ^

RESTORE: image denoising or deconvolution by local regularization.

SYNOPSIS ^

function imr=restaura2(im,method,PSF,lambdas,varargin)

DESCRIPTION ^

   RESTORE: image denoising or deconvolution by local regularization.

   RESTORE represents a local regularization framework in which no
   assumption is made on the noise nature (in particular, its variance
   may be unknown): relative weight of the regularization term is
   obtained by using the L-curve method [Hansen93]. The local
   implementation of the regularization implies that the noise may
   be non-stationary.

   In deconvolution problems, RESTORE needs the PSF information.
   Even though the formulation is local, in the current VistaRestoreTools
   version, the PSF is assumed to be spatially invariant.

   The restoration ability relies on the ability of the regularization
   functional to capture/extract/preserve the relevant features of the
   signal. A number of fixed and adaptive regularization functionals are
   provided in this function. (see J. Gutierrez et al. IEEE Tr.Im.Proc. 2006
   for details).

   SYNTAX:

        im_r=restaura2(im_d,'regulariz_functional',PSF,lambdas);

 ***Output:

  * im_r..................= restored image

 ***Input:

  * im_d..................= degraded image

  * 'regulariz_functional'= string containing the selected regularization
                            functional. The available possibilities include:

          * Classical regularization:
             - 'L2'.................: Second derivative regularization [Tikhonov79]
             - 'AR4', 'AR8', 'AR12'.: Wiener filtering with Auto-Regressive (AR) power
                                      spectrum estimation. The number in the string
                                      indicates the order of the AR model
                                      [Banham&Katsaggelos97]
             - 'CSF'................: Regularization using the inverse of the Contrast
                                      Sensitivity Function (CSF) [Hunt75].

          * Regularization based on non-linear perception models
             - 'PER'................: Divisive normalization regularization functional
                                      [Gutierrez06].
             - 'PERD'...............: Divisive normalization functional
                                      optimized for denoising

  * PSF....................= Point Spread Function as given by apply_degradation_2

  * lambdas................= Vector with the possible values of the regularization
                             parameter (relative weight of regularization vs deviation)
                             The L-curve method is used to select one of
                             these values for each block of the image.


 See details at:    J. Gutierrez et al. Regularization Operators for
                    Natural Images based on Non-linear Perception Models.
                    IEEE Trans. Im. Proc. 15(1). 2006

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 %
0002 %   RESTORE: image denoising or deconvolution by local regularization.
0003 %
0004 %   RESTORE represents a local regularization framework in which no
0005 %   assumption is made on the noise nature (in particular, its variance
0006 %   may be unknown): relative weight of the regularization term is
0007 %   obtained by using the L-curve method [Hansen93]. The local
0008 %   implementation of the regularization implies that the noise may
0009 %   be non-stationary.
0010 %
0011 %   In deconvolution problems, RESTORE needs the PSF information.
0012 %   Even though the formulation is local, in the current VistaRestoreTools
0013 %   version, the PSF is assumed to be spatially invariant.
0014 %
0015 %   The restoration ability relies on the ability of the regularization
0016 %   functional to capture/extract/preserve the relevant features of the
0017 %   signal. A number of fixed and adaptive regularization functionals are
0018 %   provided in this function. (see J. Gutierrez et al. IEEE Tr.Im.Proc. 2006
0019 %   for details).
0020 %
0021 %   SYNTAX:
0022 %
0023 %        im_r=restaura2(im_d,'regulariz_functional',PSF,lambdas);
0024 %
0025 % ***Output:
0026 %
0027 %  * im_r..................= restored image
0028 %
0029 % ***Input:
0030 %
0031 %  * im_d..................= degraded image
0032 %
0033 %  * 'regulariz_functional'= string containing the selected regularization
0034 %                            functional. The available possibilities include:
0035 %
0036 %          * Classical regularization:
0037 %             - 'L2'.................: Second derivative regularization [Tikhonov79]
0038 %             - 'AR4', 'AR8', 'AR12'.: Wiener filtering with Auto-Regressive (AR) power
0039 %                                      spectrum estimation. The number in the string
0040 %                                      indicates the order of the AR model
0041 %                                      [Banham&Katsaggelos97]
0042 %             - 'CSF'................: Regularization using the inverse of the Contrast
0043 %                                      Sensitivity Function (CSF) [Hunt75].
0044 %
0045 %          * Regularization based on non-linear perception models
0046 %             - 'PER'................: Divisive normalization regularization functional
0047 %                                      [Gutierrez06].
0048 %             - 'PERD'...............: Divisive normalization functional
0049 %                                      optimized for denoising
0050 %
0051 %  * PSF....................= Point Spread Function as given by apply_degradation_2
0052 %
0053 %  * lambdas................= Vector with the possible values of the regularization
0054 %                             parameter (relative weight of regularization vs deviation)
0055 %                             The L-curve method is used to select one of
0056 %                             these values for each block of the image.
0057 %
0058 %
0059 % See details at:    J. Gutierrez et al. Regularization Operators for
0060 %                    Natural Images based on Non-linear Perception Models.
0061 %                    IEEE Trans. Im. Proc. 15(1). 2006
0062 %
0063 function  imr=restaura2(im,method,PSF,lambdas,varargin)
0064 
0065 warning('off','MATLAB:dispatcher:InexactMatch')
0066 a=size(im);
0067 bordefil=(ceil(a(1)/16)*16-a(1));
0068 bordecol=(ceil(a(2)/16)*16-a(2));
0069 im_a = [im repmat(im(:,end),1,bordecol); repmat(im(end,:),bordefil,1) repmat(im(end,end),bordefil,bordecol)];
0070 u=0;
0071 
0072 N=16;
0073 
0074 M=32;
0075 
0076  if strcmp(method,'AR4')==1
0077         [imr_a] = regu_modelo_ar4(im_a,PSF,lambdas,N,M,2,0);
0078  elseif strcmp(method,'AR8')==1
0079         [imr_a] = regu_model_ar8(im_a,PSF,lambdas,N,M,2,0);
0080  elseif strcmp(method,'AR12')==1
0081         [imr_a] = regu_modelo_ar12(im_a,PSF,lambdas,N,M,2,0);
0082  elseif strcmp(method,'L2')==1
0083         Operador=freqz2(filt2dsegder,M,M);
0084         [imr_a] = regu_operator(im_a,PSF,lambdas,N,M,Operador,2,0);
0085  elseif strcmp(method,'CSF')==1
0086         load CSF_Operator;
0087         [imr_a] = regu_operator(im_a,PSF,lambdas,N,M,CSF_Operator,2,0);
0088  elseif strcmp(method,'PER1')==1
0089         corteResp = 800;
0090 
0091         load response_parameters;
0092 
0093         imr_a=regu_perceptual(im_a,PSF,lambdas,N,M,H,k1,k2,corteResp,2,0);
0094 elseif strcmp(method,'PER2')==1
0095         corteResp = 1000;
0096 
0097         load response_parameters;
0098 
0099         imr_a=regu_perceptual(im_a,PSF,lambdas,N,M,H,k1,k2,corteResp,2,0);
0100 elseif strcmp(method,'PER3')==1
0101         corteResp = 1600;
0102 
0103         load response_parameters;
0104 
0105         imr_a=regu_perceptual(im_a,PSF,lambdas,N,M,H,k1,k2,corteResp,2,0);
0106  else
0107         ['The requested method (' method ') is not implemented']
0108         u=1;
0109  end
0110 
0111  if u==1
0112     imr=0;
0113  else
0114     imr=imr_a(1:a(1),1:a(2));
0115  end

Generated on Fri 07-Mar-2014 13:28:33 by m2html © 2005