What techniques can I use to extract horizontal lines from noisy image data?
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Hi guys
Im trying to extract the internal layers of an ice sheet. These layers are caused by volcanic activity and are visible in ground penetrating radar measurements as horizontal lines (see example here: http://dl.dropbox.com/u/5364002/testgpr.jpg).
In the lower part of the image however, the horizontal signals are indistinguishable from the ambient noise. (see zoom of previous example here: http://dl.dropbox.com/u/5364002/testgprb.jpg).
I have been messing around with several filtering techniques both in Matlab, Photoshop and a few other photoprograms, and was wondering if anyone had any tricks up their sleeve in here, so as to unveil, or at least remove slightly from obscurity, these hidden layers.
Thanks in advance!
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Jiro Doke
il 2 Feb 2011
I agree with Kenneth. I thought this was about steganography and was about to mention a couple of links. Please rephrase the title.
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Brett Shoelson
il 3 Feb 2011
Jakob, When your signal looks a lot like your noise, separating the two is very difficult, if not impossible. But in this case, I think that you can do a reasonable job of detecting the horizontal lines. I'd start by: 1) converting to grayscale, if necessary. (Sometimes an image looks like a grayscale, but it's really RGB. Use size(img) to see if it is m x n x 3. If so, select a single color plane, or use RGB2GRAY.) 2) Use a median filter to eliminate some of the speckle noise. (MEDFILT2) 3) Try using an edge detection algorithm. (See EDGE.) Sobel and Prewitt allow you to specify the direction (horizontal or vertical) of the edges, but you might get the best results here with Canny. Note that the Canny routine allows you to specify Threshold and Sigma parameters; you'll have to play with those to get good results. (HINT: use the iterator tools in cell mode!) 4) Use Hough transforms to accumulate pixels along specified directions. (Help HOUGH, HOUGHPEAKS, HOUGHLINES.) Again, note that there are optional inputs to these functions that will be quite useful. Good luck! Brett
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Ashish Uthama
il 3 Feb 2011
Jakob, you could also try anisotropic filters in step 2. (http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik) or similar.
Image Analyst
il 24 Dic 2011
You removed your image so I can't see what it looks like, but RANSAC is really good at finding lines in extremely noisy data. See http://en.wikipedia.org/wiki/Ransac
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