perform_median_filtering.m

signal procesing toolbox

function M = perform_median_filtering(M,k)

% perform_median_filtering - perform moving average median
%
%   M = perform_median_filtering(M,k);
%
%   k is the half width of the window (detult k=1).
%
%   This filtering method is very efficient to remove impulsive or salt and
%   peper noise.
%
%   Copyright (c) 2007 Gabriel Peyre

if nargin<2
    k = 1;
end
w = 2*k+1;
n = size(M,1);

options.sampling = 'uniform';
H = compute_patch_library(M,k,options);
H = reshape(H, [w*w n*n]);
H = median(H); 
M = reshape(H, n,n);

function [H,X,Y] = compute_patch_library(M,w,options)

% [H,X,Y] = compute_patch_library(M,w,options);
%
%   M is the texture
%   w is the half-width of the patch, so that each patch
%       has size (2*w+1,2*w+1,s) where s is the number of colors.
%
%   H(:,:,:,i) is the ith patch (can be a color patch).
%   X(i) is the x location of H(:,:,:,i) in the image M.
%   Y(i) is the y location of H(:,:,:,i) in the image M.
%
%   options.sampling can be set to 'random' or 'uniform'
%   If options.sampling=='random' then you can define
%       options.nbr as the number of random patches and/or
%       option.locations_x and option.locations_y.
%
%   If w is a vector of integer size, then the algorithm 
%   compute a set of library, one for each size, 
%   and use the same location X,Y for each patch.
%   H{k} is the k-th library.
%
%   You can define options.wmax to avoid too big patch.
%   In this case, if w>wmax, the texture M will be filtered, and the patches
%   will be subsampled to match the size.
%
%   Copyright (c) 2006 Gabriel Peyr