I am trying to reconstruct an algorithm belong to this paper:

Decomposition of biospeckle images in temporary spectral bands

Here is an explanation of the algorithm:

We recorded a sequence of N successive speckle images with a sampling frequency fs. In this way it was possible to observe how a pixel evolves through the N images. That evolution can be treated as a time series and can be processed in the following way: Each signal corresponding to the evolution of every pixel was used as input to a bank of filters. The intensity values were previously divided by their temporal mean value to minimize local differences in reflectivity or illumination of the object. The maximum frequency that can be adequately analyzed is determined by the sampling theorem and s half of sampling frequency fs. The latter is set by the CCD camera, the size of the image, and the frame grabber. The bank of filters is outlined in Fig. 1. In our case, ten 5° order Butterworth11 filters were used, but this number can be varied according to the required discrimination. The bank was implemented in a computer using MATLAB software. We chose the Butter-worth filter because, in addition to its simplicity, it is maximally flat. Other filters, an infinite impulse response, or a finite impulse response could be used. By means of this bank of filters, ten corresponding signals of each filter of each temporary pixel evolution were obtained as output. Average energy Eb in each signal was then calculated:

where pb(n) is the intensity of the filtered pixel in the nth image for filter b divided by its mean value and N is the total number of images. In this way, en values of energy for each pixel were obtained, each of hem belonging to one of the frequency bands in Fig. 1. With these values it is possible to build ten images of the active object, each one of which shows how much energy of time-varying speckle there is in a certain frequency band. False color assignment to the gray levels in the results would help in discrimination.

and here is my MATLAB code base on that :

clear all

for i=0:39
     str = num2str(i);
     str1 = strcat(str,'.mat');
     load(str1);
     D{i+1}=A;
end

new_max = max(max(A));
new_min = min(min(A));

for i=20:180
    for j=20:140    
        ts = [];
        for k=1:40
            ts = [ts D{k}(i,j)]; %%% kth image pixel i,j --- ts is time series
        end
        ts = double(ts);
        temp = mean(ts);        

        ts = ts-temp;          
        ts = ts/temp;          
        N = 5; % filter order
        W = [0.00001 0.05;0.05 0.1;0.1 0.15;0.15 0.20;0.20 0.25;0.25 0.30;0.30 0.35;0.35 0.40;0.40 0.45;0.45 0.50];
        N1 = 5;                        
        for ind = 1:10            
            Wn = W(ind,:);
            [B,A] = butter(N1,Wn);            
            ts_f(ind,:) = filter(B,A,ts);            
        end        
        for ind=1:10
          imag_test1{ind}(i,j) =sum((ts_f(ind,:)./mean(ts_f(ind,:))).^2);
        end                 
    end
end

for i=1:10
 temp_imag = imag_test1{i}(:,:);
 x=isnan(temp_imag);
 temp_imag(x)=0;
 temp_imag=medfilt2(temp_imag);
 t_max = max(max(temp_imag));
 t_min = min(min(temp_imag));
 temp_imag = (temp_imag-t_min).*(double(new_max-new_min)/double(t_max-t_min))+double(new_min);
 imag_test2{i}(:,:) = temp_imag;
end

for i=1:10
    A=imag_test2{i}(:,:);
    B=A/max(max(A));
    B=histeq(B);
 figure,imshow(B)
 colorbar
end

but I am not getting the same result as paper. has anybody has aby idea why? or where I have gone wrong?

Refrence Link to the paper