sir i am working on impulse (salt and pepper) noise from fingerprint images.i am using database 1 (DB1_B) from FVC 2000 International Competition on Fingerprint Verification as test images.Please check the attached code.the result this code gives is

for j=1:20
    for i= 1:80   % Setting the variable for image under process
        % To select the image from data base DB1_B 
        select_img=['C:\Users\Sana\Desktop\progprc\DB1_B\101_',  num2str(i) '.tif'];
        %inputn=input('select image(ddf:');
        %inn=imread(inputn);
        img=imread(select_img);     
        image_1d=img(:);  
        image_double=double(image_1d);  
        % To set the frame length
        Num_pixles=length(image_double);             % Total number of pixels
        mean_power=0;   % Local mean power
        frame_length=Num_pixles;        %   withoutframmin
        T_num_frames=floor(Num_pixles/frame_length);
        
        %frame_length=480;  % Number of  pixel per frame
        
        %T_num_frames=floor(Num_pixles/frame_length); % Total number of frames
        % Setting an arrays for processed image
        new_signal = zeros(Num_pixles,1);           
        mean_array = zeros(T_num_frames,1);       
        
        
        % Setting the SNR value
        snr_vlaue=j;
        v1=mean(image_double)/(10^(snr_vlaue/10));     % snr=10log(mean of image/Var of image)
        v11=v1^2;
        u=v11/(255*255);
        
        
        % Adding noise into the selected image
        
        add_image_noise=imnoise(image_1d,'salt & pepper',u);
        image_noise_d=double(add_image_noise);
        
        % To find the frame having maximum mean power & store its Mean Power value into max_mean_array_ch 
        
        mean_array_ch=zeros(T_num_frames,1); 
        
        for ch=1 : T_num_frames                                               
            frame = image_noise_d( (ch-1)*frame_length+1: frame_length*ch);            % Taking frame from whole iamge
            mean_power_ch=0;                                                             % Zero the mean power for next calculation
            for n=1 : T_num_frames
                mean_power_ch = mean_power_ch + frame( n,1) * frame(n,1);                
            end
            mean_array_ch(ch,1)= mean_power_ch;                                           % Storing the value of Mean Power
        end
        
        max_mean_array_ch=max(mean_array_ch);   % The maximum Mean Power 
        % To set the threshold and boosting value
        th_value=(max_mean_array_ch)*0.1;       % The Threshold value
        mean_image_noise=mean(add_image_noise); % Mean of all pixels of noisy image
        boost=255/(mean_image_noise);             % The boosting value
        
        
        %calculating the mean power sum of the frame and removing that frame value which is less then threshold value
        for k=1 : T_num_frames                                               
            frame = image_noise_d( (k-1)*frame_length+1: frame_length*k);              % Taking frame from whole image
            mean_power=0;                                                            % Zero the mean power for next calculation
            for n=1 : T_num_frames
                mean_power = mean_power + frame( n,1) * frame(n,1);                             % calculating the mean power sum of the current frame
            end
            mean_array(k,1)= mean_power;                                                  % storing the current mean power sum in mean_array 
            
            if( mean_power >=th_value)                                   
          	  % If mean_power is greater than threshold value then multiply the value of that frame by boosting value 
              new_signal((k-1)*frame_length+1: frame_length*k) = (boost).*frame;
            else  
                new_signal((k-1)*frame_length+1: frame_length*k) = (0).*frame;                          
            end  
            
        end
        
        % Reconstruction of images 
        image_input=reshape(image_1d,480,640);    
        noisy_image= reshape(add_image_noise,480,640); 
        proc_image=uint8(new_signal);          
        proc_image1=reshape(proc_image,480,640);   
        %to store the noisy image
        
        file_name_noise=['C:\Users\sana\Desktop\DB\ni\' num2str(j) num2str(i) '.tif'];
        imwrite(noisy_image,file_name_noise);
        
        % To stor the processed image 
        file_name_proc=['C:\Users\sana\Desktop\DB\pi\' num2str(j)  num2str(i) '.tif'];
        imwrite(proc_image1,file_name_proc);
        
        %similarity checking
        
        ssimval=ssim(proc_image1,img);
        scaledi=mat2gray(((ssimval)*(1-eps))+eps(realmin));
        y=fprintf('ssim alue is %.4f\n',scaledi);
        
        ssimvali=ssim(noisy_image,img);
        scaledii=mat2gray(((ssimvali)*(1-eps))+eps(realmin));
        
        figure(2);
        plot(j,scaledi,'.',j,scaledii,'pg');
        hold on
        z=fprintf('ssim of nosy imae is %0.4f\n',scaledii);
    end
end

[KALYAN ACHARJYA]

Concise and clear question always adds advantage to get quick response.

Answer 1

[Image Analyst]

You forgot to attach your image. See attached salt and pepper noise removal demos.