请用MATLAB语言写出灰色序列平均弱化缓冲算子的代码

灰色序列平均弱化缓冲算子（Gray Sequence Average Weakened Buffer Operator）是一种基于灰色预测模型的图像处理算法，可以用于图像平滑和去噪。以下是MATLAB语言实现灰色序列平均弱化缓冲算子的代码：

function output_image = gray_sequence_average_weakened_buffer(input_image, weight)
% input_image: 输入图像
% weight: 灰色序列平均弱化缓冲算子的权重
% output_image: 输出图像

% 获取图像尺寸
[height, width] = size(input_image);

% 初始化输出图像
output_image = zeros(height, width);

% 对于每个像素，计算灰色序列平均弱化缓冲算子的值
for i = 2 : height - 1
    for j = 2 : width - 1
        % 构建灰色预测序列
        gray_sequence = [input_image(i-1, j), input_image(i, j-1), input_image(i-1, j-1), input_image(i-2, j), input_image(i, j-2)];
        % 计算灰色预测值
        gray_predict = gray_predictor(gray_sequence);
        % 计算灰色序列平均弱化缓冲算子的值
        output_image(i, j) = (1 - weight) * input_image(i, j) + weight * gray_predict;
    end
end

% 处理边缘像素
for i = 2 : height - 1
    % 构建灰色预测序列
    gray_sequence = [input_image(i-1, 1), input_image(i+1, 1), input_image(i, 2)];
    % 计算灰色预测值
    gray_predict = gray_predictor(gray_sequence);
    output_image(i, 1) = (1 - weight) * input_image(i, 1) + weight * gray_predict;
    % 构建灰色预测序列
    gray_sequence = [input_image(i-1, width), input_image(i+1, width), input_image(i, width-1)];
    % 计算灰色预测值
    gray_predict = gray_predictor(gray_sequence);
    output_image(i, width) = (1 - weight) * input_image(i, width) + weight * gray_predict;
end
for j = 2 : width - 1
    % 构建灰色预测序列
    gray_sequence = [input_image(1, j-1), input_image(1, j+1), input_image(2, j)];
    % 计算灰色预测值
    gray_predict = gray_predictor(gray_sequence);
    output_image(1, j) = (1 - weight) * input_image(1, j) + weight * gray_predict;
    % 构建灰色预测序列
    gray_sequence = [input_image(height, j-1), input_image(height, j+1), input_image(height-1, j)];
    % 计算灰色预测值
    gray_predict = gray_predictor(gray_sequence);
    output_image(height, j) = (1 - weight) * input_image(height, j) + weight * gray_predict;
end

% 处理四个角的像素
% 构建灰色预测序列
gray_sequence = [input_image(2, 2), input_image(1, 2), input_image(2, 1)];
% 计算灰色预测值
gray_predict = gray_predictor(gray_sequence);
output_image(1, 1) = (1 - weight) * input_image(1, 1) + weight * gray_predict;
% 构建灰色预测序列
gray_sequence = [input_image(2, width-1), input_image(1, width), input_image(2, width)];
% 计算灰色预测值
gray_predict = gray_predictor(gray_sequence);
output_image(1, width) = (1 - weight) * input_image(1, width) + weight * gray_predict;
% 构建灰色预测序列
gray_sequence = [input_image(height-1, 2), input_image(height, 2), input_image(height, 1)];
% 计算灰色预测值
gray_predict = gray_predictor(gray_sequence);
output_image(height, 1) = (1 - weight) * input_image(height, 1) + weight * gray_predict;
% 构建灰色预测序列
gray_sequence = [input_image(height-1, width), input_image(height, width-1), input_image(height, width)];
% 计算灰色预测值
gray_predict = gray_predictor(gray_sequence);
output_image(height, width) = (1 - weight) * input_image(height, width) + weight * gray_predict;

end

function predict_value = gray_predictor(sequence)
% 灰色预测模型
% sequence: 灰色预测序列
% predict_value: 灰色预测值

n = length(sequence);

% 累加生成
accumulated_sequence = cumsum(sequence);

% 紧邻平均
average_sequence = (accumulated_sequence(2:n) + accumulated_sequence(1:n-1)) / 2;

% 灰色预测模型
predict_value = (2 * sequence(1) + sum(sequence(2:n))) / (n + 2);

end

在此代码中，我们使用两个循环遍历每个像素，并构建其灰色预测序列，通过灰色预测模型计算灰色预测值，然后计算灰色序列平均弱化缓冲算子的值。为了处理边缘和角像素，我们需要进行特殊处理。