HMF.rar_hybrid median_page

function A = hmf(A,n) %HMF Hybrid median filtering. % B = HMF(A,N) performs hybrid median filtering of the matrix A using a % NxN box. Hybrid median filtering preserves edges better than a NxN % square kernel-based median filter because data from different spatial % directions are ranked separately. Three median values are calculated in % the NxN box: MR is the median of horizontal and vertical R pixels, and % MD is the median of diagonal D pixels. The filtered value is the median % of the two median values and the central pixel C: median([MR,MD,C]). % For N = 5: % |D * R * D| % |* D R D *| % |R R C R R| % |* D R D *| % |D * R * D| % % B = HMF(A) uses N = 5 (default value). % % A can be a 2-D array or an RGB image. If A is an RGB image, hybrid % median filtering is performed in the HSV color space. % % Notes % ----- % 1) N must be odd. If N is even then N is incremented by 1. % 2) The Image Processing Toolbox is required. % 3) If the function NANMEDIAN exists (Statistics Toolbox), NaN are % treated as missing values and are ignored. % % Examples % -------- % % -- original grayscale image -- % I = imread('eight.tif'); % % noisy image % J = imnoise(I,'salt & pepper',0.03); % % hybrid median filtering % K = hmf(J); % % figures % subplot(121),imshow(J),subplot(122),imshow(K) % % % -- original RGB image -- % [I,map] = imread('trees.tif'); % I = ind2rgb(I,map); % % noisy image % J = imnoise(I,'salt & pepper',0.02); % % hybrid median filtering (using a 9x9 box) % K = hmf(J,9); % % figures % subplot(121),imshow(J),subplot(122),imshow(K) % % See also MEDFILT2, MEDFILT2RGB, MEDFILT3, COLFILT % % -- Damien Garcia -- 2007/08, revised 2010/02 error(nargchk(1,2,nargin)); if nargin==1, n = 5; end if ~isscalar(n) || n<0, n = 5; end % --- n must be odd. If not then n = n+1 n = round(n); if rem(n+1,2)~=0, n = n+1; end % --- Do we have an RGB image? % RGB images can be only be uint8, uint16, single, or double isRGB = ndims(A)==3 && (isfloat(A) || isa(A,'uint8') || isa(A,'uint16')); % ---- Adapted from the obsolete function ISRGB ---- if isRGB if isfloat(A) % At first just test a small chunk to get a possible quick negative mm = size(A,1); nn = size(A,2); chunk = A(1:min(mm,10),1:min(nn,10),:); isRGB = (min(chunk(:))>=0 && max(chunk(:))<=1); % If the chunk is an RGB image, test the whole image if isRGB isRGB = (min(A(:))>=0 && max(A(:))<=1); end end end % ---- end of isrgb ---- assert(isRGB | ndims(A)==2,... 'The input must be a 2-D array or an RGB image.') classA = class(A); % --- If the input is an RGB image, HMF is used in the HSV color space if isRGB A = rgb2hsv(A); for k = 1:3, A(:,:,k) = hmf(A(:,:,k),n); end A = hsv2rgb(A); % HSV2RGB returns a double: change the class if necessary switch classA case 'uint8' A = uint8(A*255); case 'uint16' A = uint16(A*65535); case 'single' A = single(A); end return end % --- Plus & Cross masks Plus = false(n,n); Plus((n+1)/2,:) = true; Plus(:,(n+1)/2) = true; Plus = Plus(:); Cross = false(n,n); Cross((1:n)+n*(0:n-1)) = true; Cross((1:n)+n*((n-1):-1:0)) = true; Cross = Cross(:); %% --- Hybrid median filtering % Note: NANMEDIAN is used if this function exists (Statistics Toolbox) existNaNmedian = exist('nanmedian','file'); % --- the COLFILT function zero-pads! => replicate boundaries A = padarray(A,[(n-1)/2 (n-1)/2],'replicate'); M1 = colfilt(A,[n n],'sliding',@CrossMedian); M2 = colfilt(A,[n n],'sliding',@PlusMedian); if existNaNmedian A = nanmedian(cat(3,A,M1,M2),3); else A = median(cat(3,A,M1,M2),3); end % remove the borders that were added by PADARRAY A = A((n+1)/2:end-(n-1)/2,(n+1)/2:end-(n-1)/2); function CM = CrossMedian(X) ncol = size(X,2); I = repmat(Cross,[1 ncol]); X = reshape(X(I),[2*n-1 ncol]); if existNaNmedian CM = nanmedian(X); else CM = median(X); end end function PM = PlusMedian(X) ncol = size(X,2); I = repmat(Plus,[1 ncol]); X = reshape(X(I),[2*n-1 ncol]); if existNaNmedian PM = nanmedian(X); else PM = median(X); end end end