图像增强Matlab代码总结（6种）

I = imread('C:\Users\shou\Desktop\061tongtai.jpg'); tic; R = I(:, :, 1); G = I(:, :, 2); B = I(:, :, 3); R0 = double(R); G0 = double(G); B0 = double(B); [N1, M1] = size(R); Rlog = log(R0+1);%增强原理第一步取对数 Rfft2 = fft2(R0);%进行二维傅里叶变换，将R通道分量由空间域变为频率域 %sigma1 = 128; sigma1 = 2800; F1 = fspecial('gaussian', [N1,M1], sigma1);%高斯低通滤波，有两个参数，hsize表示模板尺寸，默认值为【3 3】，sigma为滤波器的标准值，单位为像素，默认值为0.5. %F1 = fspecial('unsharp', 0.2); F1为一个二维滤波器 Efft1 = fft2(double(F1));%进行傅里叶变换，将滤波器F1由空间域变为频率域 %第二步高斯模板对原图像作卷积，即相当于对原图像作低通滤波，得到低通滤波后的图像D(x,y) DR0 = Rfft2.* Efft1; %R分量和滤波器进行点乘 DR = ifft2(DR0);%进行傅里叶反变换，将频率域变为空间域 %第三步在对数域中，用原图像减去低通滤波后的图像，得到高频增强的图像G(x,y)=S’(x,y)-log(D(x,y)) DRlog = log(DR +1); Rr1 = Rlog - DRlog; sigma2 = 256; %sigma2 = 2500; 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; %sigma3 = 5100; 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; %a = 125; a = 15; II = imadd(R0, G0); II = imadd(II, B0); Ir = immultiply(R0, a); %定义彩色恢复因子C C = imdivide(Ir, II); C = log(C+1); % 将增强的分量乘以色彩恢复因子 Rr = immultiply(C, Rr); %第四步对G(x,y)取反对数，得到增强后的图像R(x,y)=exp(G(x,y)) EXPRr = exp(Rr); %第五步对R(x,y)作对比度增强，得到最终的结果图像。 MIN = min(min(EXPRr)); MAX = max(max(EXPRr)); EXPRr = (EXPRr - MIN)/(MAX - MIN); EXPRr = adapthisteq(EXPRr); % G通道处理 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; Ig = immultiply(G0, a); C = imdivide(Ig, II); C = log(C+1); Gg = immultiply(C, Gg); EXPGg = exp(Gg); MIN = min(min(EXPGg)); MAX = max(max(EXPGg)); EXPGg = (EXPGg - MIN)/(MAX - MIN); EXPGg = adapthisteq(EXPGg); %B通道处理 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; Ib = immultiply(B0, a); C = imdivide(Ib, II); C = log(C+1); Bb = immultiply(C, 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); toc; figure(); imshow(I),title('原图'); figure(); imshow(result),title('增强图'); imwrite(result,'C:\Users\shou\Desktop\061to.jpg')