MATLAB Implementation of Image Binarization Using Niblack Algorithm

If you're interested in image binarization, consider implementing the Niblack algorithm for processing. The Niblack method is a widely-used image binarization technique that determines thresholds based on local pixel intensity statistics to convert grayscale images into binary black-and-white representations. Key implementation aspects in MATLAB include: - Calculating local mean and standard deviation using sliding window operations - Implementing the threshold formula: T = m + k*s, where m is local mean, s is standard deviation, and k is a user-defined parameter - Utilizing matrix operations for efficient neighborhood processing instead of loops - Applying morphological operations for post-processing if needed You can create a MATLAB function that accepts parameters like window size and k-value for customization. For detailed implementation examples and optimized code structures, refer to relevant academic literature or image processing tutorials. This implementation serves as a practical reference for adaptive thresholding techniques in digital image processing.