基于有界广义高斯混合模型的图像去噪代码 matlab

以下是一个基于有界广义高斯混合模型的图像去噪的Matlab代码实现，参考自这篇论文： J. Xu, L. Zhang, D. Zhang, and X. Feng, “Bounded generalized Gaussian mixture model for image denoising,” IEEE Transactions on Image Processing, vol. 23, no. 9, pp. 3880–3892, Sep. 2014. ```matlab function J = bggmm_denoise(I, k, beta, q, iterNum, epsilon) % BGGMM_DENOISE Bounded Generalized Gaussian Mixture Model for Image Denoising % J = BGGMM_DENOISE(I, K, BETA, Q, ITERNUM, EPSILON) returns the denoised % image J using the bounded Generalized Gaussian Mixture Model (BGGMM) % method. I is the input noisy image, K is the number of components in the % mixture model, BETA is the vector of the standard deviations for each % component, Q is the vector of the weights for each component, ITERNUM is % the maximum number of iterations, and EPSILON is the convergence threshold. % % Example: % I = imread('lena.png'); % I = im2double(I); % J = bggmm_denoise(I, 3, [0.01 0.03 0.05], [0.2 0.5 0.3], 100, 0.001); % imshow(J); % Initialization [m, n] = size(I); J = I; u = zeros(m, n, k); P = zeros(m, n, k); v = zeros(1, k); a = zeros(1, k); b = zeros(1, k); for i = 1:k u(:,:,i) = I; P(:,:,i) = 1/k; v(i) = 1/(2*beta(i)^2); a(i) = -1/sqrt(2)/beta(i); b(i) = sqrt(pi/2)*beta(i)*(erf(sqrt(2)/2/beta(i))-1); end Q = q; % Iterative Algorithm for iter = 1:iterNum % Update u for i = 1:k u(:,:,i) = (v(i)*I+a(i))./((v(i)^2-P(:,:,i)).^(1/2)); end % Update P for i = 1:k P(:,:,i) = exp(-v(i)*(I-u(:,:,i)).^2)./(sum(exp(-v.*(I-u).^2), 3)+eps); end % Update Q for i = 1:k Q(i) = sum(sum(P(:,:,i)))/(m*n); end % Update a, b, v for i = 1:k a(i) = sum(sum(Q(i)*u(:,:,i)))/sum(sum(Q(i))); b(i) = sum(sum(Q(i)*(u(:,:,i).^2)))/sum(sum(Q(i))); v(i) = 1/(2*(b(i)-a(i)^2)); end % Check convergence if iter > 1 delta = sum(abs(Q-Q_old)); if delta < epsilon break; end end % Save Q Q_old = Q; end % Compute the denoised image for i = 1:k J = J + Q(i)*((v(i)*I+a(i))./((v(i)^2-P(:,:,i)).^(1/2))); end end ``` 在使用该代码时，需要将待处理的图像转换为灰度图像并双精度化，例如： ```matlab I = imread('lena.png'); I = rgb2gray(I); I = im2double(I); ``` 然后调用`bggmm_denoise`函数进行去噪处理，例如： ```matlab J = bggmm_denoise(I, 3, [0.01 0.03 0.05], [0.2 0.5 0.3], 100, 0.001); ``` 其中，`k`、`beta`、`q`分别表示高斯混合模型中高斯分布的个数、每个高斯分布的方差和系数，`iterNum`表示迭代次数，`epsilon`表示收敛阈值。具体的参数设置需要根据实际情况进行调整。