非局部均值信号去噪的matlab代码

以下是在MATLAB中实现的非局部均值信号去噪的示例代码：

function [denoised_signal] = nl_means_denoising(signal, patch_size, search_window_size, h)

% signal: 输入信号
% patch_size: 每个块的大小
% search_window_size: 搜索窗口的大小
% h: 控制去噪的参数

% 将信号转换为块形式
[num_rows, num_cols] = size(signal);
padded_signal = padarray(signal, [patch_size, patch_size], 'symmetric');
denoised_signal = zeros(num_rows, num_cols);

for i = 1:num_rows
    for j = 1:num_cols
        % 获取当前块的位置
        row_start = i + patch_size;
        row_end = row_start + patch_size - 1;
        col_start = j + patch_size;
        col_end = col_start + patch_size - 1;
        current_patch = padded_signal(row_start:row_end, col_start:col_end);
        
        % 计算当前块的加权平均值
        total_weight = 0;
        weighted_sum = 0;
        search_row_start = max(row_start - search_window_size, patch_size + 1);
        search_row_end = min(row_start + search_window_size, num_rows + patch_size);
        search_col_start = max(col_start - search_window_size, patch_size + 1);
        search_col_end = min(col_start + search_window_size, num_cols + patch_size);
        for m = search_row_start:search_row_end
            for n = search_col_start:search_col_end
                if m == i + patch_size && n == j + patch_size
                    continue;
                end
                neighbor_patch = padded_signal(m:(m + patch_size - 1), n:(n + patch_size - 1));
                distance = sum(sum((current_patch - neighbor_patch).^2));
                weight = exp(-distance / h^2);
                weighted_sum = weighted_sum + weight * padded_signal(m + patch_size, n + patch_size);
                total_weight = total_weight + weight;
            end
        end
        denoised_signal(i, j) = weighted_sum / total_weight;
    end
end
end

此代码实现了非局部均值信号去噪算法，其中 `signal` 是输入信号，`patch_size` 是每个块的大小，`search_window_size` 是搜索窗口的大小，`h` 是控制去噪的参数。该算法的思想是利用相似的块来对当前块进行去噪。在搜索窗口内，与当前块相似的块越多，权重越大。