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# Image Noise Reduction by Local Statistics 1.0

Date Added: July 29, 2013  |  Visits: 241

Reduce image noise by measuring local pixel statistics and remapping intensities.Tristan Ursell (c)Relative Noise TransformMarch 2012Iout=relnoise(Iin,sz,sigma);Iout=relnoise(Iin,sz,sigma,'field');[Iout,Ivar]=relnoise(Iin,sz,sigma,...);[Iout,Ivar,Imean]=relnoise(Iin,sz,sigma,...);Iin = the input image, of any numerical class.sz = (3 Inf, Iout = Iin.The field 'plot' will create an output plot comparing this transform to the original image, a Gaussian blur with STD = sz/2, and median filter with block size equal to sz. At first glance, this filter appears similar to a median transform, but it does a better job of preserving local intensity extrema. Comparison with the median filter requires the Image Processing Toolbox, but the rest of the script does not.The field 'disk' or 'square' will choose between using a disk or square filter block shape, where sz is the disk diameter or square side length. The default is square.Iout is the transformed output image.Ivar is the variance of the pixel intensities in the filter block at every point in the image -- essentially the spatially varying variance of the image.Imean is the mean smoothed image using the filter block.see also: wiener2 filter2Example:Iin=imread('spot_test.tif');Iout=relnoise(Iin,3,0.5,'square','plot');figure;imagesc(Iout-double(Iin))title('What was removed from the original image.')axis equal tightbox on

 Requirements: No special requirements Platforms: Matlab Keyword: Choose,  Default,  Diameter,  Length,  Script,  Shape,  Square Users rating: 0/10

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