设计自适应中值滤波器，通过添加高斯、盐和胡椒噪声生成噪声图像,使用所设计的自适应中值滤波器对图像进行去噪，的代码

好的，以下是使用Matlab设计自适应中值滤波器进行去噪的代码：

% 读入图像并显示
I = imread('lena.png');
figure,imshow(I);

% 添加高斯、盐和胡椒噪声
J1 = imnoise(I,'gaussian',0,0.01);
J2 = imnoise(I,'salt & pepper',0.1);
J3 = imnoise(I,'speckle',0.04);
figure,imshow(J1),title('高斯噪声');
figure,imshow(J2),title('盐和胡椒噪声');
figure,imshow(J3),title('乘性噪声');

% 自适应中值滤波器
K1 = J1;
K2 = J2;
K3 = J3;
[m,n] = size(I);
for i = 1:m
    for j = 1:n
        % 对每个像素点进行处理
        window = J1(max(1,i-2):min(m,i+2),max(1,j-2):min(n,j+2));
        Zmin = min(window(:));
        Zmax = max(window(:));
        Zmed = median(window(:));
        A1 = Zmed - Zmin;
        A2 = Zmed - Zmax;
        if A1 > 0 && A2 < 0
            B1 = J1(i,j) - Zmin;
            B2 = J1(i,j) - Zmax;
            if B1 > 0 && B2 < 0
                K1(i,j) = J1(i,j);
            else
                K1(i,j) = Zmed;
            end
        else
            K1(i,j) = Zmed;
        end
        
        window = J2(max(1,i-2):min(m,i+2),max(1,j-2):min(n,j+2));
        Zmin = min(window(:));
        Zmax = max(window(:));
        Zmed = median(window(:));
        A1 = Zmed - Zmin;
        A2 = Zmed - Zmax;
        if A1 > 0 && A2 < 0
            B1 = J2(i,j) - Zmin;
            B2 = J2(i,j) - Zmax;
            if B1 > 0 && B2 < 0
                K2(i,j) = J2(i,j);
            else
                K2(i,j) = Zmed;
            end
        else
            K2(i,j) = Zmed;
        end
        
        window = J3(max(1,i-2):min(m,i+2),max(1,j-2):min(n,j+2));
        Zmin = min(window(:));
        Zmax = max(window(:));
        Zmed = median(window(:));
        A1 = Zmed - Zmin;
        A2 = Zmed - Zmax;
        if A1 > 0 && A2 < 0
            B1 = J3(i,j) - Zmin;
            B2 = J3(i,j) - Zmax;
            if B1 > 0 && B2 < 0
                K3(i,j) = J3(i,j);
            else
                K3(i,j) = Zmed;
            end
        else
            K3(i,j) = Zmed;
        end
    end
end

% 显示去噪后的图像
figure,imshow(K1),title('高斯噪声去噪后');
figure,imshow(K2),title('盐和胡椒噪声去噪后');
figure,imshow(K3),title('乘性噪声去噪后');

其中，自适应中值滤波器的实现是按照算法步骤逐个像素点进行处理，判断窗口内的像素是否需要进行中值滤波。最终得到去噪后的图像，可以与添加噪声前的原始图像进行对比。