Could somebody help me with MATLAB implementation of 'A simple fuzzy method to remove mixed Gaussian-impulsive noise from color images'

Abstract— Mixed impulsive and Gaussian noise reduction from digital colour images is a challenging task because it is necessary to appropriately process both types of noise that in turn need to be distinguished from the original image structures such as edges and details. Fuzzy theory is useful to build simple, efficient and effective solutions for this problem. In this paper, we propose a fuzzy method to reduce Gaussian and impulsive noise from colour images. Our method uses one only filtering operation: a weighted averaging. A fuzzy rule system is used to assign the weights in the averaging so that both noise types are reduced and image structures are preserved. We provide experimental results to show that the performance of the method is competitive with respect to state-of-the-art filters.

[jerin]

So far i got Impulse noise removal based on fuzzy. Now i need code to remove Gaussian noise using fuzzy.

Thanks to MATLAB File exchange and code provider,

   % Fuzzy Technique for Image Impulse Noice Reduction

clear all
close all
clc

%First read the image from file 
org = imread('org.bmp','bmp');
%for adding salt n peper impulse noise\/
sample = imnoise(org,'salt & pepper',0.05);
%for adding Gaussian noise, uncomment\/
%  sample = imnoise(org,'gaussian',0,0.02);

%creating noisy image
imwrite(sample,'input.bmp');

%Reading noisy image
sample = imread('input.bmp','bmp');
% output = imread('input.bmp','bmp'); 

sample(1,:) = org(1,:);
sample(2,:) = org(2,:);
sample(:,1) = org(:,1);
sample(:,2) = org(:,2);

sample(199,:) = org(199,:);
sample(200,:) = org(200,:);
sample(:,199) = org(:,199);
sample(:,200) = org(:,200);
%obs1 = imread('input.bmp','bmp');

% Offset to measure the size of pixel matrix
K = 1; 

% No of noisy pixels found in the image
victims1 = 0;
victims2 = 0;

epsilon = 20;

% Place the input to the output matrix before manipulation
      for x = 1:length(sample)
          for y = 1:length(sample)
              obs1(x,y) = 0;
              temp(x,y) = 0;
          end
      end

      % **************** First Detection Unit 
      % Step 1
      obs2 = Step1(sample);

      % Step 2
      obs1 = Step2(obs2);

      %Differentiation
      for x = K + 1:length(sample)- K % Rows
          for y = K + 1:length(sample) - K % Columns
              % Compute differences
              temp(x,y) =  abs( double(obs1(x,y)) - double(obs2(x,y)) );
          end
      end
      %Fuzzy Rule 1 
      [obs1,victims1] = FuzzyRule1(temp,epsilon); 

      % **************** Second Detection Unit 
      %Fuzzy Rule 2 & 3 Gradients
      [obs2,victims21,victims22] = FuzzyRule2n3(sample,epsilon);

      common = 0;
      for x = 1:length(sample)
            for y = 1:length(sample)
                if obs1(x,y) > 0 | obs2(x,y) > 0
                    temp(x,y) = 255;
                    common = common + 1;
                else
                    temp(x,y) = -1;
                end                  
           end
      end

      % Write the Noise distributions into the respective files
      imwrite( obs1,'NoiseDistribution_DetectionMothode_1.bmp');
      imwrite( obs2,'NoiseDistribution_DetectionMothode_2.bmp');
      imwrite( temp,'NoiseDistribution_DetectionMothode_Both.bmp');

      output = ApplyOriginalFilter(temp,sample);
  %     output = ApplyFilter(temp,sample);
      imwrite( output,'output.bmp');

      victims1,victims21,common
  % 
      scrsz = get(0,'ScreenSize');
      figure('Position',[1 scrsz(4) scrsz(3) scrsz(4)])
      subplot(2,3,1), subimage(org)
      title('Original Image');
      subplot(2,3,2), subimage(sample)
      title('Noisy Image');
      subplot(2,3,3), subimage(obs1)    
      title('Noise Distribution Detection Method 1');
      subplot(2,3,4), subimage(obs2)    
      title('Noise Distribution Detection Method 2');
      subplot(2,3,5), subimage(temp)    
      title('Noise Distribution Detection Both');
      subplot(2,3,6), subimage(output)    
      title('Final Output');

      scrsz = get(0,'ScreenSize');
      figure('Position',[10 200 scrsz(3)/2 scrsz(4)-200])
      subplot(1,2,1), subimage(sample)
      title('Noisy Image');
      subplot(1,2,2), subimage(output)
      title('Final Output');

      scrsz = get(0,'ScreenSize');
      figure('Position',[scrsz(3)/2 200 scrsz(3)/2 scrsz(4)-200])
      subplot(1,2,1), subimage(org)
      title('Original Image');
      subplot(1,2,2), subimage(output)
      title('Final Output');

[Cuong Eric]

1. Why are you using epsilon = 20; 2. Can you give me a file ApplyFilter(temp,sample);?

[Hari Prasad]

What are the functions Step1() and Step2() ?

[SHUAB KHAN]

When I'm trying to run this code it's showing error because of Step1 and Step2 are not defined. How can we define these?