remove periodical like lines from the image

As you can see, that blurs the image a fair bit. What might perhaps work better is to subtract the mean of each row. This will only work if the bands are perfectly alligned to the grid.

figure

im3=im-mean(im,2);

imshow(im3,[])

You can fine-tune this second method by using a moving average instead of averaging the entire image.

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Dimani4 2022-12-27

Exactly, I want to remove the striping.

Dimani4 2022-12-27

在 MATLAB Online 中打开

Just want to share the result I got from movmean(image,50,2)

im4=im-movmean(im,50,2);

Thank you for your suggestion, Rik. :)

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Answer 2

Image Analyst 2022-12-27

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We really need to know what gave rise to the lines. For example are you using a line scan camera and the lighting is flickering?

What I'd do is to take the mean of the image horizontally and divide the image by the mean. That would compensate for the case where the row of the image is good, it's just the wrong brightness.

S = load('matrix2see')

S = struct with fields:

picture: [203×256 double]

grayImage = S.picture;

% imwrite(mat2gray(grayImage), 'dimani4.png')

[rows, columns, numberOfColorChannels] = size(grayImage)

rows = 203

columns = 256

numberOfColorChannels = 1

subplot(1,2,1)

imshow(grayImage,[])

title('Original Image')

rowMeans = mean(grayImage, 2);

repairedImage = grayImage ./ repmat(rowMeans, [1, columns]);

subplot(1,2,2)

imshow(repairedImage,[])

title('Repaired Image')

It looks like the rows means are not perfect, either because the lighting changes as the scan goes across, or the presence of particles in there affects the mean, changing it from it's true value. If you could get the means from a totally blank shot, it would help, but that assumes the pattern of lines stays fixed from one shot to the next.

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Dimani4 2022-12-29

编辑：Dimani4 2022-12-29

Thank you for your answer.

No, unfortunately the lines are not appear in the same location for every image.I cannot do the scan without sample because the tip during the scan should be in contact with the sample (AFM- Atomic Force Uscope), So these lines happened because the scan was performed in noisy environment (people were chatting during the scan).

As usual with these files people using WSxM program to remove noises from the picture and analyse pictures and so on. So Subtract Line Filter in WSxm looks similar as the movmean function of Matlab to this particular picture. Look at the attached figures.

I added .stp file in .zip which can be opened in WSxm program.

Image Analyst 2022-12-29

If you're happy with the result, that's fine. The moving mean will blur the image more than a moving median. But maybe the blur is not important for what you need to do with the image. If it's really important you can rescan the sample and tell everyone not to talk.

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Answer 3

George Heath 2024-10-9

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https://ww2.mathworks.cn/matlabcentral/answers/1884852-remove-periodical-like-lines-from-the-image#answer_1529515

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Hi Dimani,

An alternative way is fit 1st or 2nd order polynomials to each line:

img =picture;
polyx = 1; %change to 2 for 2nd order polynomial 
xl = 1:size(img,2);
for i =1:size(img,1)
y1 = img(i,:);
x1 =  xl;
[p,~,mu] = polyfit(x1,y1,polyx);
result(i,:) = img(i,:) - polyval(p,1:size(img,2),[],mu);
end
imagesc(result)

This can then be refined with thresholding to exclude high or low regions, this example using the otsu method to auto threshold:

min_max(2) = multithresh(result,1);
min_max(1) = -inf;
imgt = (result<=min_max(2)).*(result>=min_max(1));
imgt(imgt==0) = NaN;
xl = 1:size(result,2);
for i =1:size(result,1)
pos = imgt(i,:)>0;
if sum(pos)>polyx+5
y1 = result(i,pos);
x1 =  xl(pos);
[p,~,mu] = polyfit(x1,y1,2);
result2(i,:) = result(i,:) - polyval(p,1:size(result,2),[],mu);
else
result2(i,pos) = result(i,pos);
end
end
imagesc(result2)

The full codes for plane and line leveling are avalible in a GUI and as source code (see filter_img_v2)

https://uk.mathworks.com/matlabcentral/fileexchange/154880-nanolocz

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