[image denoising] image denoising based on mean + median + Gauss low pass + various improved wavelet transform, including Matlab source code

1 brief introduction

The image denoising method combining mean filtering and wavelet transform is to decompose the noisy image by wavelet first , In the wavelet domain , Select an appropriate threshold to process the wavelet coefficients , Then the image signal is locally reconstructed to the first layer , Different templates are used to filter the mean value of each sub image of the first layer , Finally, the denoised image is synthesized from the low-frequency approximate image and the high-frequency detail image after mean filtering . This method is related to the global Donoho soft , Hard threshold wavelet denoising method and method Birge-Massart Soft strategy , Compared with the hard threshold wavelet denoising method , Its denoising effect is more obvious . It reduces the noise of the image while , And keep the details of the image as much as possible , And the image is smoother . Simulation results show that , This method is an effective image denoising method .

2 Part of the code

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       function X = wdenoise(x, measure, sorh, thr, wname, n)
       
       
%  Threshold denoising function 
       
       
%   x Is a noisy image  
       
       
%   measure Indicates global or local 
       
       
%   sorh Represents the soft and hard threshold method 
       
       
%   thr Threshold value 
       
       
%   wname Is the name of the wavelet function 
       
       
%   n For the decomposition hierarchy 
       
       
[C, S] = wavedec2(x, n, wname);                 %  Wavelet decomposition of the image 
       
       
switch measure
       
       
    case 'gbl'                                  %  Global threshold method 
       
       
        dcoef = C( prod(S(1, :)) + 1 : end);    %    Extract the detail part coefficient 
       
       
        switch sorh
       
       
            case 'h'                            %    Hard threshold 
       
       
                dcoef = dcoef .* (abs(dcoef) > thr);
       
       
            case 's'                            %    Soft threshold 
       
       
                temp = abs(dcoef) - thr;
       
       
                temp = (temp + abs(temp)) / 2;
       
       
                dcoef = sign(dcoef) .* temp;
       
       
        end
       
       
        C( prod(S(1, :)) + 1 : end) = dcoef;
       
       
    case 'lvd'                                  %  Local threshold method 
       
       
        for i = n:-1:1                          %    Each layer is treated separately 
       
       
            k = size(S,1) - i;
       
       
            first = prod(S(1, :)) + ...
       
       
                3 * sum(S(2:k-1, 1) .* S(2:k-1, 2)) + 1;    
       
       
                                                %    The first i The starting position of the layer detail factor 
       
       
            last = first + 3*prod(S(k,:)) - 1;  %    Termination position 
       
       
            dcoef = C(first : last);            %    Detail factor 
       
       
        switch sorh
       
       
            case 'h'                            %    Hard threshold 
       
       
                dcoef = dcoef .* (abs(dcoef) > thr(i));
       
       
            case 's'                            %    Soft threshold 
       
       
                temp = abs(dcoef) - thr(i);
       
       
                temp = (temp + abs(temp)) / 2;
       
       
                dcoef = sign(dcoef) .* temp;
       
       
        end
       
       
            C(first:last) = dcoef;
       
       
        end
       
       
end
       
       
X = waverec2(C, S, wname);                      %  Reconstruct the image 
      
      
      
      

       
       
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3 Simulation results

4 reference

[1] Peng Shushu . Image denoising based on mean filter and wavelet transform [J]. Modern computers , 2019(12):6.

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