结合全局阈值法和局部阈值法优点, 提出一种基于图像增强的双阈值二值化方法。利用OTSU和Kittler法计算出的两个阈值, 对灰度值大于高阈值和小于低阈值的点预先处理, 再对灰度值处于双阈值之间的点用Sauvola法进行判断。双阈值不仅保留了更多细节信息, 还控制了算法的时间开销, 但是仍没有解决光照不均的图像二值化。针对此问题, 提出了一种新的图像增强算法。利用均分的4块区域与整幅图像的平均灰度值的4个差值, 结合与像素点图像位置相关的4个权值, 对图像上各个点进行相对应的灰度补偿, 反复增强, 直至4块区域的平均灰度值控制在一定范围内。经过图像增强后, 光照不均图像二值化效果有了明显的改善。

In order to combine the advantages of global threshold with local threshold method, a double-threshold binarization method based on image enhancement was proposed. The two thresholds were calculated out by using OTSU and Kittler method, and preprocessing was made to the points with gray value greater than the upper threshold or less than the lower threshold. Sauvola method was used to judge the points with gray value between the two thresholds. Double-threshold remained more detailed information with less time cost, but still had the problem of uneven illumination. Therefore, a new image enhancement algorithm was proposed. The image was divided into four areas equally, and the differences of the average grey value of the whole image with that of each area were taken. Then by using the four weights related to pixel points'location, the corresponding gray value on each point was compensated. Image enhancement was made repeatedly until the average gray value of the four areas was controlled within a certain range. The binarization of uneven illumination image was improved greatly after this image enhancement.