原图
结果图
处理过程
①傅里叶变换：对原图的三个通道进行傅里叶变换
②带阻滤波器：设计一个椭圆形的带阻滤波器
③滤波：对傅里叶变换的结果进行滤波
④逆傅里叶变换：对滤波后的结果进行逆傅里叶变换
⑤图像增强：使用二阶全变分算法对逆傅里叶变换的结构进行去噪，二阶全变分算法能够有效的平滑平坦区域，保留图像边缘细节。
MATLAB代码：
clear;close all;clc;img_rgb = imread('figure.jpg');figure,imshow(img_rgb,[]);W=50;n=5;d0=112;[l1,l2,l3]=size(img_rgb);img_out = zeros(l1,l2,l3);for k=1:3img=im2double(img_rgb(:,:,k));img_fft=fft2(img);g=fftshift(img_fft);[N1,N2]=size(g);Home = zeros(N1, N2);n1=round(N1/2);n2=round(N2/2);for i=1:N1 for j=1:N2d=sqrt(((i-n1)*N2/N1)^2+((j-n2))^2);Home(i,j)=1/(1+((d*W)/(d^2-d0^2))^(2*n));endendy=g.*Home; if k==1%figure,imshow(g,[]);figure,imshow(log(abs(g)+1),[]);figure,imshow(Home,[]);figure,imshow(log(abs(y)+1),[]);endy=ifftshift(y);E1=ifft2(y);img_out(:,:,k)=real(E1);end figure,imshow(img_out,[]), title('处理后图像');%用一个TV_L2算子对处理结果进行去噪img_out_filter = TV_L2_filter(img_out);figure,imshow(img_out_filter,[]), title('处理后图像'); imwrite(img_out_filter,'.\result.jpg')
TV_L2_filter.m
function [S] = TV_L2_filter(Im, lambda, kappa, betamax)if ~exist('kappa','var')kappa = 2.0;endif ~exist('lambda','var')lambda = 2e-2;endif ~exist('betamax','var')betamax = 1e5;endIm = im2double(Im);S = Im;fx = [1, -1];fy = [1; -1];[N,M,D] = size(Im);sizeI2D = [N,M];otfFx = psf2otf(fx,sizeI2D);otfFy = psf2otf(fy,sizeI2D);Normin1 = fft2(S);Denormin2 = abs(otfFx).^2 + abs(otfFy ).^2;if D>1Denormin2 = repmat(Denormin2,[1,1,D]);endbeta = 2*lambda;while beta < betamaxlambeta = lambda/beta;Denormin = 1 + beta*Denormin2;% h-v subproblemu = [diff(S,1,2), S(:,1,:) - S(:,end,:)];v = [diff(S,1,1); S(1,:,:) - S(end,:,:)];u = max(abs(u)-lambeta,0).*sign(u);v = max(abs(v)-lambeta,0).*sign(v);% S subproblemNormin2 = [u(:,end,:) - u(:, 1,:), -diff(u,1,2)];Normin2 = Normin2 + [v(end,:,:) - v(1, :,:); -diff(v,1,1)];FS = (Normin1 + beta*fft2(Normin2))./Denormin;S = real(ifft2(FS));beta = beta*kappa;endend
如果觉得《使用带阻滤波器去除周期性噪声》对你有帮助，请点赞、收藏，并留下你的观点哦！
