lecture04medFilter - neighborhood median, not mean Noisy...

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Notes on image processing Mean versus median filtering
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Images need to be cleaned  up: Histogram equalization Left image does not use all available gray levels. Image is recoded so that  all gray levels are used and such that each gray level occurs in roughly the  same number of pixels of the recoded image. (See algorithm in text, xv.)
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Compute output pixel using  neighborhood of pixels in input Input image A Output image B Pixel B[j,k] Neighborhood of  A[j,k] MEAN filtering:   B[j,k] is the average of the neighborhood of A[j,k] MEDIAN filtering: B[j,k] is the median of the neighborhood of A[j,k]
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Linear smoothing smoothes  noise and blurs signal One row of mean filtered image
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Median filter replaces center with 
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Unformatted text preview: neighborhood median, not mean Noisy row of checkers image Mean filtering smoothes signal and ramps the boundary Median filter smoothes signal and preserves sharp boundary Scratches removed by using a median filter Using the mean to remove the streaks will also blur the entire image. Need to implement median filter efficiently, because sorting the neighborhood can be slow Can use bucket idea Relate to text problem 7.38 Updating the array of neighborhood values becomes critical For 7x7 neighborhood, for next pixel, we delete 7 from the set and add 7, the other 49-14 elements are the same...
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This note was uploaded on 07/25/2008 for the course CSE 331 taught by Professor M.mccullen during the Spring '08 term at Michigan State University.

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lecture04medFilter - neighborhood median, not mean Noisy...

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