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  • 2017-05-03 16:54
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包含有8中滤波方法,可对含有噪声的数字图像执行包括laplacian滤波,prewitt滤波,sobel滤波等方法的去噪声处理。
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  • S_filter.m
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  • filters.m
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function h = S_filter(varargin) % H = S_FILTER(TYPE) creates a two-dimensional filter H of the % specified type. % % H = S_FILTER('average',HSIZE) returns an averaging filter H of size % HSIZE. HSIZE can be a vector specifying the number of rows and columns in % H or a scalar, in which case H is a square matrix. % The default HSIZE is [3 3]. % % H = S_FILTER('disk',RADIUS) returns a circular averaging filter % (pillbox) within the square matrix of side 2*RADIUS+1. % The default RADIUS is 5. % % H =S_FILTER FSPECIAL('gaussian',HSIZE,SIGMA) returns a rotationally % symmetric Gaussian lowpass filter of size HSIZE with standard % deviation SIGMA (positive). HSIZE can be a vector specifying the % number of rows and columns in H or a scalar, in which case H is a % square matrix. % The default HSIZE is [3 3], the default SIGMA is 0.5. % % H =S_FILTER FSPECIAL('laplacian',ALPHA) returns a 3-by-3 filter % approximating the shape of the two-dimensional Laplacian % operator. The parameter ALPHA controls the shape of the % Laplacian and must be in the range 0.0 to 1.0. % The default ALPHA is 0.2. % % H = S_FILTER('log',HSIZE,SIGMA) returns a rotationally symmetric % Laplacian of Gaussian filter of size HSIZE with standard deviation % SIGMA (positive). HSIZE can be a vector specifying the number of rows % and columns in H or a scalar, in which case H is a square matrix. % The default HSIZE is [5 5], the default SIGMA is 0.5. % % H = S_FILTER('motion',LEN,THETA) returns a filter to approximate, once % convolved with an image, the linear motion of a camera by LEN pixels, % with an angle of THETA degrees in a counter-clockwise direction. The % filter becomes a vector for horizontal and vertical motions. The % default LEN is 9, the default THETA is 0, which corresponds to a % horizontal motion of 9 pixels. % % H = S_FILTER('prewitt') returns 3-by-3 filter that emphasizes % horizontal edges by approximating a vertical gradient. If you need to % emphasize vertical edges, transpose the filter H: H'. % % [1 1 1;0 0 0;-1 -1 -1]. % % H = S_FILTER('sobel') returns 3-by-3 filter that emphasizes % horizontal edges utilizing the smoothing effect by approximating a % vertical gradient. If you need to emphasize vertical edges, transpose % the filter H: H'. % % [1 2 1;0 0 0;-1 -2 -1]. % % Class Support % ------------- % H is of class double. [type, p2, p3] = ParseInputs(varargin{:}); switch type case 'average' % Smoothing filter siz = p2; h = ones(siz)/prod(siz); case 'disk' % Disk filter rad = p2; crad = ceil(rad-0.5); [x,y] = meshgrid(-crad:crad,-crad:crad); maxxy = max(abs(x),abs(y)); minxy = min(abs(x),abs(y)); m1 = (rad^2 < (maxxy+0.5).^2 + (minxy-0.5).^2).*(minxy-0.5) + ... (rad^2 >= (maxxy+0.5).^2 + (minxy-0.5).^2).* ... sqrt(rad^2 - (maxxy + 0.5).^2); m2 = (rad^2 > (maxxy-0.5).^2 + (minxy+0.5).^2).*(minxy+0.5) + ... (rad^2 <= (maxxy-0.5).^2 + (minxy+0.5).^2).* ... sqrt(rad^2 - (maxxy - 0.5).^2); sgrid = (rad^2*(0.5*(asin(m2/rad) - asin(m1/rad)) + ... 0.25*(sin(2*asin(m2/rad)) - sin(2*asin(m1/rad)))) - ... (maxxy-0.5).*(m2-m1) + (m1-minxy+0.5)) ... .*((((rad^2 < (maxxy+0.5).^2 + (minxy+0.5).^2) & ... (rad^2 > (maxxy-0.5).^2 + (minxy-0.5).^2)) | ... ((minxy==0)&(maxxy-0.5 < rad)&(maxxy+0.5>=rad)))); sgrid = sgrid + ((maxxy+0.5).^2 + (minxy+0.5).^2 < rad^2); sgrid(crad+1,crad+1) = min(pi*rad^2,pi/2); if ((crad>0) && (rad > crad-0.5) && (rad^2 < (crad-0.5)^2+0.25)) m1 = sqrt(rad^2 - (crad - 0.5).^2); m1n = m1/rad; sg0 = 2*(rad^2*(0.5*asin(m1n) + 0.25*sin(2*asin(m1n)))-m1*(crad-0.5)); sgrid(2*crad+1,crad+1) = sg0; sgrid(crad+1,2*crad+1) = sg0; sgrid(crad+1,1) = sg0; sgrid(1,crad+1) = sg0; sgrid(2*crad,crad+1) = sgrid(2*crad,crad+1) - sg0; sgrid(crad+1,2*crad) = sgrid(crad+1,2*crad) - sg0; sgrid(crad+1,2) = sgrid(crad+1,2) - sg0; sgrid(2,crad+1) = sgrid(2,crad+1) - sg0; end sgrid(crad+1,crad+1) = min(sgrid(crad+1,crad+1),1); h = sgrid/sum(sgrid(:)); case 'gaussian' % Gaussian filter siz = (p2-1)/2; std = p3; [x,y] = meshgrid(-siz(2):siz(2),-siz(1):siz(1)); arg = -(x.*x + y.*y)/(2*std*std); h = exp(arg); h(h<eps*max(h(:))) = 0; sumh = sum(h(:)); if sumh ~= 0, h = h/sumh; end; case 'laplacian' % Laplacian filter alpha = p2; alpha = max(0,min(alpha,1)); h1 = alpha/(alpha+1); h2 = (1-alpha)/(alpha+1); h = [h1 h2 h1;h2 -4/(alpha+1) h2;h1 h2 h1]; case 'log' % Laplacian of Gaussian % first calculate Gaussian siz = (p2-1)/2; std2 = p3^2; [x,y] = meshgrid(-siz(2):siz(2),-siz(1):siz(1)); arg = -(x.*x + y.*y)/(2*std2); h = exp(arg); h(h<eps*max(h(:))) = 0; sumh = sum(h(:)); if sumh ~= 0, h = h/sumh; end; % now calculate Laplacian h1 = h.*(x.*x + y.*y - 2*std2)/(std2^2); h = h1 - sum(h1(:))/prod(p2); % make the filter sum to zero case 'motion' % Motion filter uses bilinear interpolation len = max(1,p2); half = (len-1)/2;% rotate half length around center phi = mod(p3,180)/180*pi; cosphi = cos(phi); sinphi = sin(phi); xsign = sign(cosphi); linewdt = 1; % define mesh for the half matrix, eps takes care of the right size % for 0 & 90 rotation sx = fix(half*cosphi + linewdt*xsign - len*eps); sy = fix(half*sinphi + linewdt - len*eps); [x y] = meshgrid(0:xsign:sx, 0:sy); % define shortest distance from a pixel to the rotated line dist2line = (y*cosphi-x*sinphi);% distance perpendicular to the line rad = sqrt(x.^2 + y.^2); % find points beyond the line's end-point but within the line width lastpix = find((rad >= half)&(abs(dist2line)<=linewdt)); %distance to the line's end-point parallel to the line x2lastpix = half - abs((x(lastpix) + dist2line(lastpix)*sinphi)/cosphi); dist2line(lastpix) = sqrt(dist2line(lastpix).^2 + x2lastpix.^2); dist2line = linewdt + eps - abs(dist2line); dist2line(dist2line<0) = 0;% zero out anything beyond line width % unfold half-matrix to the full size h = rot90(dist2line,2); h(end+(1:end)-1,end+(1:end)-1) = dist2line; h = h./(sum(h(:)) + eps*len*len); if cosphi>0, h = flipud(h); end case 'prewitt' % Prewitt filter h = [1 1 1;0 0 0;-1 -1 -1]; case 'sobel' % Sobel filter h = [1 2 1;0 0 0;-1 -2 -1]; case 'unsharp' % Unsharp filter alpha = p2; h = [0 0 0;0 1 0;0 0 0] - fspecial('laplacian',alpha); end %%% %%% ParseInputs %%% function [type, p2, p3] = ParseInputs(varargin) % default values p2 = []; p3 = []; % Check the number of input arguments. narginchk(1,3); % Determine filter type from the user supplied string. type = varargin{1}; type = validatestring(type,{'gaussian','sobel','prewitt','laplacian','log',... 'average','unsharp','disk','motion'},mfilename,'TYPE',1); % default values switch type case 'average' p2 = [3 3]; % siz case 'disk' p2 = 5; % rad case 'gaussian' p2 = [3 3]; % siz p3 = 0.5; % std case {'laplacian', 'unsharp'} p2 = 1/5; % alpha case 'log' p2 = [5 5]; % siz p3 = 0.5; % std case 'motion' p2 = 9; % len p3 = 0; % theta end
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