Implementation of Bayer Dithering Algorithm for Image Binarization Processing

In this text, we can implement image binarization processing using the Bayer dithering algorithm. This algorithm converts images into strictly black and white colors by comparing pixel intensities against a predefined threshold matrix, making the processed images more suitable for frequency-modulated screening output. The core implementation involves generating a Bayer ordered dither matrix (typically 2x2, 4x4, or 8x8) where thresholds are distributed to create dispersed dot patterns. Key functions would include matrix normalization and pixel-wise comparison: for each pixel, the algorithm calculates threshold = matrix(x % n, y % n) * (max_intensity / matrix_size) and outputs white if pixel_value > threshold, otherwise black. Additionally, by adjusting algorithm parameters such as matrix size and intensity scaling factors, users can optimize processing effects to achieve superior binarization results. Therefore, employing the Bayer dithering algorithm plays a vital role in image processing workflows, enhancing both image quality and visual outcomes through controlled dot distribution patterns.