Zhang and Tan's Enhanced Niblack Method for Improved Image Binarization

Zhang and Tan developed an enhanced version of the Niblack method that significantly improves image binarization accuracy. This approach combines both global and local image information to determine optimal thresholds, effectively addressing several limitations present in the original Niblack algorithm. In experimental evaluations, their method demonstrated superior performance, particularly when processing images with significant noise and uneven illumination conditions. The algorithm implementation typically involves calculating local mean and standard deviation within a sliding window while incorporating global statistical measures to adaptively adjust threshold values. This enhanced binarization technique has found applications across multiple domains including document text recognition systems and facial detection algorithms, where robust segmentation under challenging lighting conditions is crucial. The method's core functionality can be implemented using image processing libraries like OpenCV through calculated weighted combinations of local and global statistical features.