apply_degradetion_2

PURPOSE ^

function [Id,PSF] = apply_degradetion_2(Im,Type,varargin);

SYNOPSIS ^

function [degraded,PSF] = apply_degradetion(Im,type,varargin);

DESCRIPTION ^

 function [Id,PSF] = apply_degradetion_2(Im,Type,varargin);

  APPLY_DEGRADATON_2 degrades the input image according to some
  degradation model. The available degradation models include:

     * 'wt_gn'      : White Gaussian Noise
     * 'col_gn'     : Colored Gaussian Noise
     * 'blur_wt_gn' : Blur plus white Gaussian Noise
     * 'blur_col_gn': Blur plus colored Gaussian Noise
     * 'jpeg'       : JPEG degradation due to compression
     * 'salt_pepper': Salt and Pepper Noise.

  The number of input arguments depends on the degradation model
  They have to be included in the described order:

     * 'wt_gn'      : White Gaussian Noise
                      Needs (1) the variance of the noise
     * 'col_gn'     : Colored Gaussian Noise
                      Needs (1) the frequency band limits of the noise
                                [f_min f_max] expresed in normalized units
                                (in the range [0,1], where 1 is the
                                Nyquist frequency).
                            (2) The variance of the noise

     * 'blur_wt_gn' : Blur plus white Gaussian Noise
                      Needs (1) the cut-off frequency of the blurring
                                operator (in normalized units).
                            (2) the minimum value of the blurring operator
                                (in the range [0,1]).
                                In order to preserve some information of
                                high frequency components, the user can
                                introduce a minimum value for the amplitude
                                of the blurring filter (maximum attenuation
                                factor).
                                Value 0 means that the Gaussian low-pass
                                operator will not be modified.
                            (3) The variance of the noise.

     * 'blur_col_gn': Blur plus colored Gaussian Noise
                      Needs (1) the frequency band limits of the noise
                                [f_min f_max] expresed in normalized units
                                (in the range [0,1], where 1 is the
                                Nyquist frequency).
                            (2) the cut-off frequency of the blurring
                                operator (in normalized units).
                            (3) the minimum value of the blurring operator
                                (in the range [0,1]).
                                In order to preserve some information of
                                high frequency components, the user can
                                introduce a minimum value for the amplitude
                                of the blurring filter (maximum attenuation
                                factor).
                                Value 0 means that the Gaussian low-pass
                                operator will not be modified.
                            (4) The variance of the noise.

     * 'jpeg'       : JPEG degradation due to compression
                      Needs (1) the quality parameter (see imwrite)

     * 'salt_pepper': Salt and Pepper Noise.
                      Needs (1) noise density (see imnoise)

   OUTPUT:
   Id : degraded image
   PSF: the Point Spread Function of the blurring (this can be just
        a delta function if there is no blurring).

   SYNTAX:

   [degraded,PSF] = apply_degradetion_2(Im,'Degradat_Model',Param1,Param2,...);

 J. Gutierrez, V. Laparra, G. Camps and J. Malo. "VistaRestoreTools: an image
 restoration toolbox for Matlab", http://www.uv.es/vista/vistavalencia/software.html

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 % function [Id,PSF] = apply_degradetion_2(Im,Type,varargin);
0002 %
0003 %  APPLY_DEGRADATON_2 degrades the input image according to some
0004 %  degradation model. The available degradation models include:
0005 %
0006 %     * 'wt_gn'      : White Gaussian Noise
0007 %     * 'col_gn'     : Colored Gaussian Noise
0008 %     * 'blur_wt_gn' : Blur plus white Gaussian Noise
0009 %     * 'blur_col_gn': Blur plus colored Gaussian Noise
0010 %     * 'jpeg'       : JPEG degradation due to compression
0011 %     * 'salt_pepper': Salt and Pepper Noise.
0012 %
0013 %  The number of input arguments depends on the degradation model
0014 %  They have to be included in the described order:
0015 %
0016 %     * 'wt_gn'      : White Gaussian Noise
0017 %                      Needs (1) the variance of the noise
0018 %     * 'col_gn'     : Colored Gaussian Noise
0019 %                      Needs (1) the frequency band limits of the noise
0020 %                                [f_min f_max] expresed in normalized units
0021 %                                (in the range [0,1], where 1 is the
0022 %                                Nyquist frequency).
0023 %                            (2) The variance of the noise
0024 %
0025 %     * 'blur_wt_gn' : Blur plus white Gaussian Noise
0026 %                      Needs (1) the cut-off frequency of the blurring
0027 %                                operator (in normalized units).
0028 %                            (2) the minimum value of the blurring operator
0029 %                                (in the range [0,1]).
0030 %                                In order to preserve some information of
0031 %                                high frequency components, the user can
0032 %                                introduce a minimum value for the amplitude
0033 %                                of the blurring filter (maximum attenuation
0034 %                                factor).
0035 %                                Value 0 means that the Gaussian low-pass
0036 %                                operator will not be modified.
0037 %                            (3) The variance of the noise.
0038 %
0039 %     * 'blur_col_gn': Blur plus colored Gaussian Noise
0040 %                      Needs (1) the frequency band limits of the noise
0041 %                                [f_min f_max] expresed in normalized units
0042 %                                (in the range [0,1], where 1 is the
0043 %                                Nyquist frequency).
0044 %                            (2) the cut-off frequency of the blurring
0045 %                                operator (in normalized units).
0046 %                            (3) the minimum value of the blurring operator
0047 %                                (in the range [0,1]).
0048 %                                In order to preserve some information of
0049 %                                high frequency components, the user can
0050 %                                introduce a minimum value for the amplitude
0051 %                                of the blurring filter (maximum attenuation
0052 %                                factor).
0053 %                                Value 0 means that the Gaussian low-pass
0054 %                                operator will not be modified.
0055 %                            (4) The variance of the noise.
0056 %
0057 %     * 'jpeg'       : JPEG degradation due to compression
0058 %                      Needs (1) the quality parameter (see imwrite)
0059 %
0060 %     * 'salt_pepper': Salt and Pepper Noise.
0061 %                      Needs (1) noise density (see imnoise)
0062 %
0063 %   OUTPUT:
0064 %   Id : degraded image
0065 %   PSF: the Point Spread Function of the blurring (this can be just
0066 %        a delta function if there is no blurring).
0067 %
0068 %   SYNTAX:
0069 %
0070 %   [degraded,PSF] = apply_degradetion_2(Im,'Degradat_Model',Param1,Param2,...);
0071 %
0072 % J. Gutierrez, V. Laparra, G. Camps and J. Malo. "VistaRestoreTools: an image
0073 % restoration toolbox for Matlab", http://www.uv.es/vista/vistavalencia/software.html
0074 %
0075 function [degraded,PSF] = apply_degradetion(Im,type,varargin);
0076 
0077 warning('off','MATLAB:dispatcher:InexactMatch')
0078 if (strcmpi(type,'wt_gn'))
0079 
0080    fc = 3;
0081    Val_min = 0.001;
0082 
0083    lim_frec = [0 3];
0084 
0085    variance = varargin{1};
0086 
0087    [degraded,PSF,H,noise,Blurred]=degrade(Im,fc,Val_min,lim_frec,variance);
0088 
0089 elseif  (strcmpi(type,'col_gn'))
0090 
0091    fc = 3;
0092    Val_min = 0.001;
0093 
0094    lim_frec = varargin{1};
0095 
0096    variance = varargin{2};
0097 
0098    [degraded,PSF,H,noise,Blurred]=degrade(Im,fc,Val_min,lim_frec,variance);
0099 
0100 elseif  (strcmpi(type,'blur_wt_gn'))
0101 
0102    fc = varargin{1};
0103    Val_min = varargin{2};
0104 
0105    lim_frec = [0 3];
0106 
0107    variance = varargin{3};
0108 
0109    [degraded,PSF,H,noise,Blurred]=degrade(Im,fc,Val_min,lim_frec,variance);
0110 
0111 elseif  (strcmpi(type,'blur_col_gn'))
0112 
0113    fc = varargin{1};
0114    Val_min = varargin{2};
0115 
0116    lim_frec = varargin{3};
0117 
0118    variance = varargin{4};
0119 
0120    [degraded,PSF,H,noise,Blurred]=degrade(Im,fc,Val_min,lim_frec,variance);
0121 
0122 elseif  (strcmpi(type,'jpeg'))
0123 
0124    PSF = zeros(11);
0125    PSF(6,6)=1;
0126 
0127    imwrite(Im/255,'tmp.jpg','Quality',varargin{1});
0128    degraded = imread('tmp.jpg');
0129 
0130 elseif  (strcmpi(type,'salt_pepper'))
0131 
0132    PSF = zeros(11);
0133    PSF(6,6)=1;
0134    degraded = imnoise(Im/255,'salt & pepper', varargin{1});
0135    degraded = degraded*255;
0136 else
0137    error('The requested degradation is not implemented');
0138 end
0139 
0140 degraded = double(degraded);

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