- Do you want to delete those columns, which would make the image narrower?
- Do you want to set columns with lines in them to black or some other intensity?
- Do you want to fill in the stripes with values from each side of the stripe?
How to repair the vertical lines ?
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Hi, I have some grayscale images with some white vertical line. I wanted to rid them. I don't know which method exactly work for it. Previously I have applied Savitzky-Golay filter with vertical median filter. However, it provide a low quality image at the end of the process. Please help me for doing it.
Please find the attached for an sample image
2 commentaires
Image Analyst
le 19 Avr 2022
Modifié(e) : Image Analyst
le 19 Avr 2022
Exactly what does "rid" mean to you?
What gives rise to the line? Why are most stripes brighter in the bottom of the image than the top of the image?
Réponse acceptée
DGM
le 19 Avr 2022
Modifié(e) : DGM
le 20 Avr 2022
Not sure what features of the image are important, so I'll just throw this simple approach out there.
A = imread('https://www.mathworks.com/matlabcentral/answers/uploaded_files/966550/test.png');
A = im2double(A);
Amn = mean(A,1); % vertical mean
Amn = Amn-mean(Amn,2); % vertical mean - global mean
B = A-Amn; % suppress stripes
imshow(B)
Depending on the needs, you might want to clamp Amn to only represent cases where the vertical mean exceeds the global mean.
Amn = mean(A,1);
Amn = imclamp(Amn-mean(Amn,2));
B = A-Amn;
imshow(B)
You could also restrict your sample region to the lower half of the image:
Amn = mean(A(120:end,:),1); % only look at lower half
Amn = imclamp(Amn-mean(Amn,2));
B = A-Amn;
imshow(B)
You could also look at the relative column averages in the upper and lower halves and interpolate. That might help, as the variations aren't uniform over the height of the image.
Amnu = mean(A(1:120,:),1); % only look at upper half
Amnu = Amnu-mean(Amnu,2);
Amnl = mean(A(120:end,:),1); % only look at lower half
Amnl = Amnl-mean(Amnl,2);
Amn = imresize([Amnu; Amnl],size(A),'bilinear');
B = A-Amn;
imshow(B)
Similarly, with intermediate clamping:
Amnu = mean(A(1:120,:),1); % only look at upper half
Amnu = imclamp(Amnu-mean(Amnu,2));
Amnl = mean(A(120:end,:),1); % only look at lower half
Amnl = imclamp(Amnl-mean(Amnl,2));
Amn = imresize([Amnu; Amnl],size(A),'bilinear');
B = A-Amn;
imshow(B)
... but if we're going to try to get local with the averaging, what about a local average filter? We can just do something similar to what imflatfield() does and divide by the local mean.
% use division to remove local trend instead
fk = ones(50,1);
blurred = imfilter(A,fk/sum(fk(:)),'replicate');
bmn = mean(blurred(~isnan(blurred)));
B = bmn*A./blurred;
imshow(B)
I have no idea if any of these are technically appropriate, so I'm just going to throw out a bunch of stuff and let you pick and choose what parts you think are suited for the task.
3 commentaires
Plus de réponses (1)
yanqi liu
le 19 Avr 2022
yes,sir,may be use image pixel peaks to find line,such as
img = imread('https://ww2.mathworks.cn/matlabcentral/answers/uploaded_files/966550/test.png');
rs = sum(img);
rs = smooth(rs, 5);
ind = find(rs>max(rs)*0.8);
rs2 = zeros(size(rs));
rs2(ind) = rs(ind);
[pks,locs] = findpeaks(rs2);
figure; plot(rs);
hold on;
plot(ind, rs(ind),'r.')
plot(locs, pks, 'go');
figure; imshow(img, []);
hold on;
for i = 1 : length(locs)
plot([locs(i) locs(i)], [1 size(img,1)], 'r-', 'LineWidth', 2);
end
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