input_image = imread('圖片地址.tif');
% Step 1 計算每個閾值級別的 AMBE
hist = imhist(input_image); % 計算直方圖
total_pixels = numel(input_image);% 獲取圖像中的總像素數n
ambe_values = zeros(256, 1); % 初始化數組以便儲存AMBE的值
% 根據閾值將輸入的直方圖一分為二
for threshold =[]
lower_hist = hist(1:threshold + 1);
upper_hist = hist(threshold + 2:end);
% 檢查是否有任何直方圖為空
if isempty(lower_hist) || isempty(upper_hist)
ambe_values(threshold + 1) = inf; %將AMBE設置為無限大
else
lower_cdf = cumsum(lower_hist);
upper_cdf = cumsum(upper_hist);% 計算nk
% 按像素總數歸一化Pr（rk）=nk/n
lower_cdf_normalized = lower_cdf / total_pixels;
upper_cdf_normalized = upper_cdf / total_pixels;
% 計算兩個區域的平均強度
mean_lower = sum((0:threshold) .* lower_hist) / sum(lower_hist);
mean_upper = sum((threshold + 1:255) .* upper_hist) / sum(upper_hist);
% 計算絕對平均亮度誤差 （AMBE）
ambe_values(threshold + 1) = abs(mean_upper - mean_lower);
end
end
% STEP 2 找到產生最小 MBE 的閾值水平 XT
[~, optimal_threshold] = min(ambe_values);
XT = optimal_threshold - 1;
% Step 3 根據找到的 XT將輸入直方圖一分為二，獨立均衡化
% 根據最佳閾值分割圖像
lower_region = input_image <= XT;
upper_region = input_image > XT;
% 兩個區域單獨直方圖均衡化
lower_eq = histeq(input_image(lower_region), 256);
upper_eq = histeq(input_image(upper_region), 256);
% 合并兩個均衡化區域得出最終結果
result_image = input_image;
result_image(lower_region) = lower_eq;
result_image(upper_region) = upper_eq;
% 顯示最終圖像&直方圖
figure
imshow(input_image)
title('原圖像');
figure
imshow(result_image)
title('圖像MMBEBHE的結果');
figure
imhist(input_image);
title('原圖像的直方圖');
figure
imhist(result_image);
title('圖像MMBEBHE的直方圖')
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