tongtailvbo.zip_tongtailvbo_光照处理_同态滤波 光照

clear all;

% 装载图片,将 yourimage 换成你要处理的图片

img= imread('1.jpg');

%img=img0(:,:,1);

% 显示这个图片

J=rgb2gray(img);

figure,imshow(J); title('灰度图');

figure,imhist(J);

figure,imshow(img);

matsize = size(gauss_low_filter);

% 由于同态滤波是要滤出高频部分,

% 所以我们得把这个低通滤波器转换成一个高通滤波器. % f_high 和 f_low 是控制这个高通

滤波器形态的参数.

gauss_high_filter = zeros(matsize);

gauss_high_filter(ceil(matsize(1,1)/2) , ceil(matsize(1,2)/2)) = 1.0;

gauss_high_filter = f_high*gauss_high_filter - (f_high-f_low)*gauss_low_filter;

% 显示高通滤波期的频率响应

figure,freqz2(gauss_high_filter);

colormap(jet(64));

title('巴特沃斯高通滤波结果');

% 由于被处理的图象是经过对数变换的,我们再用幂变换将图象恢复过来

high_part = exp(high_log_part);

minv = min(min(high_part));

maxv = max(max(high_part));

rimg(:,:,1)=(high_part(:,:,1)-minv(1,1,1))/(maxv(1,1,1)-minv(1,1,1));

rimg(:,:,2)=(high_part(:,:,2)-minv(1,1,2))/(maxv(1,1,2)-minv(1,1,2));

rimg(:,:,3)=(high_part(:,:,3)-minv(1,1,3))/(maxv(1,1,3)-minv(1,1,3));

% 得到的结果图象

figure,imshow(uint8(255*rimg));