可以生成CLAHE算法的MATLAB代码吗

当然可以，下面是一个简单的CLAHE算法的MATLAB代码示例：

function img_out = clahe(img_in, clipLimit, tileSize)
% img_in: 输入图像
% clipLimit: 对比度限制参数
% tileSize: 分块大小

% 将图像转换为灰度图像
if size(img_in,3) == 3
    img_in = rgb2gray(img_in);
end

% 计算图像尺寸
[h, w] = size(img_in);

% 计算分块数量
numTilesH = ceil(h/tileSize);
numTilesW = ceil(w/tileSize);

% 计算分块大小
tileH = ceil(h/numTilesH);
tileW = ceil(w/numTilesW);

% 计算每个分块的直方图
histogram = zeros(256, numTilesH*numTilesW);
for i = 1:numTilesH
    for j = 1:numTilesW
        rowStart = (i-1)*tileH+1;
        rowEnd = min(i*tileH, h);
        colStart = (j-1)*tileW+1;
        colEnd = min(j*tileW, w);
        tile = img_in(rowStart:rowEnd, colStart:colEnd);
        [hist, ~] = imhist(tile);
        histogram(:, (i-1)*numTilesW+j) = hist;
    end
end

% 对每个分块进行直方图均衡化
for i = 1:numTilesH
    for j = 1:numTilesW
        rowStart = (i-1)*tileH+1;
        rowEnd = min(i*tileH, h);
        colStart = (j-1)*tileW+1;
        colEnd = min(j*tileW, w);
        tile = img_in(rowStart:rowEnd, colStart:colEnd);
        hist = histogram(:, (i-1)*numTilesW+j);
        cdf = cumsum(hist)/sum(hist);
        clipHist = hist;
        clipHist(clipHist > clipLimit) = clipLimit;
        clipHist = clipHist*(sum(hist)/sum(clipHist));
        clipCDF = cumsum(clipHist)/sum(clipHist);
        tile_out = zeros(size(tile));
        for ii = 1:size(tile,1)
            for jj = 1:size(tile,2)
                pixel = tile(ii,jj);
                pixelCDF = clipCDF(pixel+1);
                pixel_out = round(pixelCDF*255);
                tile_out(ii,jj) = pixel_out;
            end
        end
        img_out(rowStart:rowEnd, colStart:colEnd) = tile_out;
    end
end

% 显示原始图像和增强后的图像
figure
subplot(1,2,1)
imshow(img_in)
title('Original Image')
subplot(1,2,2)
imshow(img_out)
title('CLAHE Image')
end

这段代码实现了CLAHE算法的主要流程，包括图像分块、直方图计算、直方图均衡化、对比度限制和图像拼接等步骤。需要注意的是，这只是一个简单的示例代码，实际使用时可能需要根据具体情况进行调整和优化。