visushrink去噪.zip_Visushrink_小波_小波 去噪_小波去噪_小波域

I = imread('1.bmp'); Inoise = imnoise(I,'gaussian',0,0.01); imwrite(Inoise,'2.bmp'); Inoi = double(Inoise); [a,b] = size(Inoi); [c,s]=wavedec2(Inoi,2,'coif2'); % w = wave2gray(c,s,2); % figure(1);imshow(w) n=[1,2]; % 尺度向量 d1 = detcoef2('compact',c,s,1); % 提取第一层的高频系数 d2 = detcoef2('compact',c,s,2); % 提取第二层的高频系数 % d3 = detcoef2('compact',c,s,3); % 提取第三层的高频系数 % d4 = detcoef2('compact',c,s,4); % 提取第四层的高频系数 %% VisuShrink法求阈值 d1t=abs(d1); N1 = length(d1); % 第一层高频系数的个数 N2 = length(d2); % 第二层高频系数的个数 % N3 = length(d3); % N4 = length(d4); v1=median(d1t)/0.6745; % 第一层高频噪声的均方差估计 r1=v1*sqrt(2*log(N1))/2; % 第一层阈值 r2=v1*sqrt(2*log(N2))/3; % 第一层阈值 % r3=v1*sqrt(1*log(N3))/4; % r4=v1*sqrt(1*log(N4))/5; p=[r1,r2]; % 设置阈值向量 % 各尺度去噪 nc=wthcoef2('h',c,s,n,p,'s'); % 对各个尺度进行阈值去噪 nc=wthcoef2('d',nc,s,n,p,'s'); nc=wthcoef2('v',nc,s,n,p,'s'); % 图像重构 I1=waverec2(nc,s,'coif2'); % 小波重构 I1re=uint8(I1); %% stationary wavelet transform order_sw = 2; [A H V D] = swt2(Inoise,order_sw,'haar'); for q = 1:order_sw swt1_A(:,:,q) = wiener2(A(:,:,q),[3 3]); swt1_H(:,:,q) = wiener2(H(:,:,q),[9 9]); swt1_V(:,:,q) = wiener2(V(:,:,q),[9 9]); swt1_D(:,:,q) = wiener2(D(:,:,q),[9 9]); % ans = A(:,:,1)-swt1_A end for k=1:order_sw swX = iswt2(A(:,:,k),H(:,:,k,:),V(:,:,k,:),D(:,:,k,:),'haar')/512; end %% 计算MSE和PSNR %visushrink m1 = 1:a; n1 = 1:b; mse1 = mse( I,I1 ) psnr1 = psnr( I,I1 ) %% 画图 figure(2); subplot(2,2,1); imshow(I); title('原图像'); subplot(2,2,2); imshow(Inoise); title('噪声图'); subplot(2,2,3); imshow(I1re); title('VisuShrink阈值'); subplot(2,2,4);imshow(swX); title('SWT')