三大白平衡算法的统一实现gray world,gray edge,width patch，Shades of Grey（matl...

% general_cc: estimates the light source of an input_image. % % Depending on the parameters the estimation is equal to Grey-Wolrd, Max-RGB, general Grey-World, % Shades-of-Gray or Grey-Edge algorithm. % % SYNOPSIS: % [white_R ,white_G ,white_B,output_data] = general_cc(input_data,njet,mink_norm,sigma,mask_im) % % INPUT : % input_data : color input image (NxMx3) % njet : the order of differentiation (range from 0-2). % mink_norm : minkowski norm used (if mink_norm==-1 then the max % operation is applied which is equal to minkowski_norm=infinity). % mask_im : binary images with zeros on image positions which % should be considered for illuminant estimation. % OUTPUT: % [white_R,white_G,white_B] : illuminant color estimation % output_data : color corrected image % LITERATURE : % % J. van de Weijer, Th. Gevers, A. Gijsenij % "Edge-Based Color Constancy" % IEEE Trans. Image Processing, accepted 2007. % % The paper includes references to other Color Constancy algorithms % included in general_cc.m such as Grey-World, and max-RGB, and % Shades-of-Gray. function [white_R ,white_G ,white_B,output_data] = general_cc(input_data,njet,mink_norm,sigma,mask_im) if(nargin<2), njet=0; end if(nargin<3), mink_norm=1; end if(nargin<4), sigma=1; end if(nargin<5), mask_im=zeros(size(input_data,1),size(input_data,2)); end %原图大小的0矩阵 % remove all saturated points saturation_threshold = 255; mask_im2 = mask_im + (dilation33(double(max(input_data,[],3)>=saturation_threshold))); %膨胀操作 留下=255的地方 mask_im2=double(mask_im2==0); mask_im2=set_border(mask_im2,sigma+1,0); % the mask_im2 contains pixels higher saturation_threshold and which are % not included in mask_im. output_data=input_data; if(njet==0) if(sigma~=0) for ii=1:3 input_data(:,:,ii)=gDer(input_data(:,:,ii),sigma,0,0); end end end if(njet>0) [Rx,Gx,Bx]=norm_derivative(input_data, sigma, njet); input_data(:,:,1)=Rx; input_data(:,:,2)=Gx; input_data(:,:,3)=Bx; end input_data=abs(input_data); if(mink_norm~=-1) % minkowski norm = (1,infinity > kleur=power(input_data,mink_norm); %img ^ mink_norm white_R = power(sum(sum(kleur(:,:,1).*mask_im2)),1/mink_norm);% mink_norm=1 白色区域的和 white_G = power(sum(sum(kleur(:,:,2).*mask_im2)),1/mink_norm); white_B = power(sum(sum(kleur(:,:,3).*mask_im2)),1/mink_norm); som=sqrt(white_R^2+white_G^2+white_B^2);%空间平均 white_R=white_R/som; white_G=white_G/som; white_B=white_B/som; else %minkowski-norm is infinit: Max-algorithm R=input_data(:,:,1); G=input_data(:,:,2); B=input_data(:,:,3); white_R=max(R(:).*mask_im2(:)); white_G=max(G(:).*mask_im2(:)); white_B=max(B(:).*mask_im2(:)); som=sqrt(white_R^2+white_G^2+white_B^2); white_R=white_R/som; white_G=white_G/som; white_B=white_B/som; end output_data(:,:,1)=output_data(:,:,1)/(white_R*sqrt(3)); output_data(:,:,2)=output_data(:,:,2)/(white_G*sqrt(3)); output_data(:,:,3)=output_data(:,:,3)/(white_B*sqrt(3));