Remove vertical stripping noise
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Hello,
The main goal here is to view the concentric circles and measure the distance between them (by cross sectioning using 'improfile'). However, the noise is getting in the way and making the distance estimation very difficult. I have been trying to remove the vertical stripping noise from the image attached...but couldn't get anywhere... Any hint what kind of filter would be useful?
Depending on the result of noise removal I might need to do some object recognition to visualize the circles in a proper way...but that's for later.
Thanks in advance. Kind regards, Ahmad
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David Young
2014년 9월 17일
You can smooth the image in the vertical direction, which reduces the amplitude of the circles but leaves the stripes. Then subtract this from the original image, to leave just the circles and the borders at the top and bottom. You can't achieve perfect separation, but this gets quite a good result.
You can do the smoothing with fspecial and convn (or conv2 if you first convert the image to grayscale). Alternatively, you can use gsmoothn from here, as in the code below:
im = imread('NO AIRGAP2.bmp');
imd = double(im);
sigma = 10; % experiment with this parameter to get best result
imcircles = imd - gsmoothn(imd, [sigma 0 0], 'Region', 'same');
% display the result, converting to grayscale to make display clearer
imshow(mean(imcircles, 3), []);
I suspect there may be a very good solution possible using morphological operations instead of linear ones, but I haven't explored that.
댓글 수: 3
David Young
2014년 9월 18일
Ah, it's a MATLAB version issue. Change addParamValue to addParameter and it will probably be OK.
추가 답변 (1개)
Iain
2014년 9월 18일
Here's an answer that probably hits what you need...
column_offsets = median(im);
column_offsets = column_offsets - min(column_offsets);
new_im = bsxfun(@minus,im,column_offsets);
댓글 수: 2
David Young
2014년 9월 18일
Did you try it? I just did, and it didn't seem to work very well, I think because the stripes aren't exactly along the columns. Also you need to convert the image to grayscale before you apply the process, otherwise you get an error in the second line.
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