matlab - artifacts in processed images

itemprop="text">

This question is related to my
previous post href="https://stackoverflow.com/questions/10863734/image-processing-algorithm-in-matlab">Image
Processing Algorithm in Matlab in stackoverflow, which I already got the
results that I wanted to.

But now I am facing
another problem, and getting some artefacts in the process images. In my original images
(stack of 600 images) I can't see any artefacts, please see the original image from
finger nail:

src="https://i.stack.imgur.com/5hM3u.png" alt="enter image description
here">

But in my 10 processed results I can
see these lines:

src="https://i.stack.imgur.com/QO2yM.png" alt="enter image description
here">

I really don't know where they come
from?

Also if they belong to the camera's
sensor why can't I see them in my original images? Any
idea?

Edit:

I
have added the following code suggested by @Jonas. It reduces the artefact, but does not
completely remove
them.

%averaging of
            images
im = D{1}(:,:);
for i = 2:100
 im =
            imadd(im,D{i}(:,:));
end
im =
            im/100;
imshow(im,[]);

for
            i=1:100

SD{i}(:,:)=imsubtract(D{i}(:,:),im(:,:))
end

@belisarius
has asked for more images, so I am going to upload 4 images from my finger with speckle
pattern and 4 images from black background size( 1280x1024
):

src="https://i.stack.imgur.com/0dPEl.png" alt="image1">
src="https://i.stack.imgur.com/8rzW1.png" alt="image2">
src="https://i.stack.imgur.com/gjUcq.png" alt="image3">
src="https://i.stack.imgur.com/UiuKK.png"
alt="iamge4">

And here is the
black background:

src="https://i.stack.imgur.com/ENVl1.png" alt="blackbackground1">
src="https://i.stack.imgur.com/RCx97.png" alt="blackbackground2">
src="https://i.stack.imgur.com/JcW9X.png"
alt="blackbackground3">

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class="normal">Answer

Here is an
answer that in opinion will remove the lines more gently than the above mentioned
methods:

im = imread('image.png');
            % Original image

imFiltered = im; % The filtered image will end up
            here
imChanged = false(size(im));% To document the filter performance
            

% 1)
% Compute the histgrams for each column in the
            lower part of the image
% (where the columns are most clear) and compute the
            mean and std each
% bin in the histogram.
histograms =
            hist(double(im(501:520,:)),0:255);
colMean =
            mean(histograms,2);
colStd =
            std(histograms,0,2);


% 2)
% Now loop though
            each gray level above zero and...
for grayLevel = 1:255


            % Find the columns where the number of 'graylevel' pixels is larger than
 %
            mean_n_graylevel + 3*std_n_graylevel). - That is columns that contains
 %
            statistically 'many' pixel with the current 'graylevel'. 
 lineColumns =
            find(histograms(grayLevel+1,:)>colMean(grayLevel+1)+3*colStd(grayLevel+1));



            % Now remove all graylevel pixels in lineColumns in the original image

            if(~isempty(lineColumns))
 for col = lineColumns 
 imFiltered(:,col)
            = im(:,col).*uint8(~(im(:,col)==grayLevel));
 imChanged(:,col) =
            im(:,col)==grayLevel;
 end
 end
            
end

imshow(imChanged)

figure,imshow(imFiltered)

Here
is the image after filtering

src="https://i.stack.imgur.com/CZjIX.png" alt="Filtered
Image">

And this shows the pixels affected by
the filter

src="https://i.stack.imgur.com/lhBth.png" alt="Pixels affected by
filter">