Local Edge Detection Techniques for Image Processing: Sauvola and Niblack Algorithms

In image processing, local edge detection enables superior handling of intricate image details. The Sauvola and Niblack algorithms represent two prominent local thresholding techniques. The Sauvola algorithm dynamically adjusts thresholds to accommodate varying illumination conditions across images, while the Niblack algorithm establishes a fixed-size window around each pixel and computes thresholds based on local pixel statistics. These algorithms typically involve calculating local mean and standard deviation values within sliding windows, with Sauvola incorporating an additional dynamic range parameter (R) for better illumination adaptation. Key implementation considerations include window size selection, border handling strategies, and optimization for computational efficiency. These advanced thresholding methods find practical applications in digital libraries, printed material processing, document scanning systems, and various image analysis domains where accurate edge preservation is critical.