sharpening image and removing noise
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Ben11 on 25 Jul 2014
Edited: Ben11 on 25 Jul 2014
If you have the Image Processing Toolbox (to check if you do type 'ver' in the command window), look up "fspecial" which is very handy.
1) Among others you can use a median, mean or gaussian filter (i.e. low-pass) to smooth the image and reduce noise. The median is less sensitive to outliers than the gaussian and preserves considerable details. You can implement this filter like the following using medfilt2, which requires a grayscale image so you need either to use rgb2gray(YourImage) or take individual channels:
for Channel = 1:3
MedianFiltered(:,:,Channel) = medfilt2(YourImage(:,:,Channel),[Radius Radius]);
where [Radius Radius] can be modified to span larger areas from which to compute the median. Larger radius yields more blurry images with less details. You can also compute other filters with "fspecial" I mentionned above, like mean or gaussian filters.
If you don't have the Image Processing Toolbox you can make up your own mean filter, for example, using convolution like the following. More info about convolution can be found on Wikipedia here
% Build a 3 x 3 kernel, for example, used to apply the mean filter:
kernel = ones(3,3)/9;
FilteredImage = conv2(YourImage,kernel,'same'); % Keep the size of your original image.
2) The second part of your question looks like an image segmentation problem. Look here for nice examples on the Mathworks website.
Hope that helps!
Spandan Tiwari on 26 Jul 2014
Also for the image sharpening part, you can use the function IMSHARPEN in the Image Processing Toolbox. It has parameters that you can try to change to sharpen legitimate edges in the image and not noise (although that's not always possible).