同态滤波处理光照不均匀图像Matlab代码

clear all;

% 装载图片,将yourimage换成你要处理的图片

img= imread('无标.png');

%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);

title('Original Image');

% 构造一个高斯滤波器

f_high = 1.0;

f_low = 0.4;

% 得到一个高斯低通滤波器

gauss_low_filter = fspecial('gaussian', [7 7], 1.414);

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));

% 利用对数变换将入射光和反射光部分分开

log_img = log(double(img));

% 将高斯高通滤波器与对数转换后的图象卷积

high_log_part = imfilter(log_img, gauss_high_filter, 'symmetric', 'conv'); % 显示卷积后的图象

figure,imshow(uint8(255*((high_log_part-1.5)/(2.4-1.5))),[]);

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));

title('同态滤波结果');

h=rgb2gray(uint8(255*rimg));

figure,imhist(h);

H=medfilt2(J); %中值滤波

figure,imshow(H);title('中值滤波处理'); %显示中值滤波后的图像