【图像去雾】基于matlab多尺度Retinex图像去雾【含Matlab源码 3549期】.zip

I = imread('test4.jpg'); %读取图像 R = I(:, :, 1); %R通道的二维图像 G = I(:, :, 2); %G通道的二维图像 B = I(:, :, 3); %B通道的二维图像 R0 = im2double(R); %将图像的数据类型转换为double类型 G0 = im2double(G); B0 = im2double(B); [N1, M1] = size(R); a = 50; %MSRCR算法中的调整参数 II = imadd(R0, G0); II = imadd(II, B0); %将R、G、B三个通道的图叠加在一起 Ir = immultiply(R0, a); %R通道的图像乘以调整参数 Ig = immultiply(G0, a); %G通道的图像乘以调整参数 Ib = immultiply(B0, a); %B通道的图像乘以调整参数 Rlog = log(R0+1); Rfft2 = fft2(R0); sigma1 = 128; %尺度参数之一 F1 = fspecial('gaussian', [N1,M1], sigma1); %建立高斯低通滤波算子 Efft1 = fft2(double(F1)); DR0 = Rfft2.* Efft1; %滤波算子和原始R通道图像进行卷积，得到空间平滑图像 DR = ifft2(DR0); DRlog = log(DR +1); Rr1 = Rlog - DRlog; %R通道图像减去空间平滑图像，得到R通道的物体反射特性 sigma2 = 256; %尺度参数之一，同sigma1 F2 = fspecial('gaussian', [N1,M1], sigma2); Efft2 = fft2(double(F2)); DR0 = Rfft2.* Efft2; DR = ifft2(DR0); DRlog = log(DR +1); Rr2 = Rlog - DRlog; sigma3 = 512; F3 = fspecial('gaussian', [N1,M1], sigma3); Efft3 = fft2(double(F3)); DR0 = Rfft2.* Efft3; DR = ifft2(DR0); DRlog = log(DR +1); Rr3 = Rlog - DRlog; Rr = (Rr1 + Rr2 +Rr3)/3; %为三个尺度取权重因子 alpha = imdivide(Ir, II); % alpha为彩色恢复因子 alpha = log(alpha+1); Rr = immultiply(alpha, Rr); %将彩色恢复因子与得到的R通道物体反射特性相乘 EXPRr = exp(Rr); MIN = min(min(EXPRr)); MAX = max(max(EXPRr)); EXPRr = (EXPRr - MIN)/(MAX - MIN); %将输出量化 EXPRr = adapthisteq(EXPRr); %进行直方图均衡 %G通道与R通道实现思路相同 Glog = log(G0+1); Gfft2 = fft2(G0); DG0 = Gfft2.* Efft1; DG = ifft2(DG0); DGlog = log(DG +1); Gg1 = Glog - DGlog; DG0 = Gfft2.* Efft2; DG = ifft2(DG0); DGlog = log(DG +1); Gg2 = Glog - DGlog; DG0 = Gfft2.* Efft3; DG = ifft2(DG0); DGlog = log(DG +1); Gg3 = Glog - DGlog; Gg = (Gg1 + Gg2 +Gg3)/3; alpha = imdivide(Ig, II); alpha = log(alpha+1); Gg = immultiply(alpha, Gg); EXPGg = exp(Gg); MIN = min(min(EXPGg)); MAX = max(max(EXPGg)); EXPGg = (EXPGg - MIN)/(MAX - MIN); EXPGg = adapthisteq(EXPGg); %B通道与R通道实现思路相同 Blog = log(B0+1); Bfft2 = fft2(B0); DB0 = Bfft2.* Efft1; DB = ifft2(DB0); DBlog = log(DB +1); Bb1 = Blog - DBlog; DB0 = Bfft2.* Efft2; DB = ifft2(DB0); DBlog = log(DB +1); Bb2 = Blog - DBlog; DB0 = Bfft2.* Efft3; DB = ifft2(DB0); DBlog = log(DB +1); Bb3 = Blog - DBlog; Bb = (Bb1 + Bb2 +Bb3)/3; alpha = imdivide(Ib, II); alpha = log(alpha+1); Bb = immultiply(alpha, Bb); EXPBb = exp(Bb); MIN = min(min(EXPBb)); MAX = max(max(EXPBb)); EXPBb = (EXPBb - MIN)/(MAX - MIN); EXPBb = adapthisteq(EXPBb); result = cat(3, EXPRr, EXPGg, EXPBb); %将R、G、B三个图像的矩阵连结在一起 figure; imshow(I);title('原图') %输出原图像 figure; imshow(result); title('多尺度Retinex理论去雾后') %输出处理后的图