MATLAB Implementation of BM3D Algorithm for Image Denoising

BM3D (Block-Matching and 3D Filtering) algorithm is an intuitive yet powerful approach for image denoising. The implementation involves grouping similar image patches into 3D arrays, applying collaborative filtering in transform domain, and aggregating results to reconstruct the clean image. The algorithm effectively reduces various types of noise while preserving important image structures and textures. Key implementation components include: - Block matching procedure to find similar patches using Euclidean distance calculations - 3D transform processing using wavelet or cosine transforms for sparse representation - Hard-thresholding and Wiener filtering stages for noise reduction - Patch aggregation with weighted averaging to reconstruct the final image The code includes PSNR (Peak Signal-to-Noise Ratio) calculation to quantitatively assess denoising performance by comparing the processed image with the original noise-free reference. This implementation helps improve image clarity and quality through advanced collaborative filtering techniques, making it suitable for practical image restoration applications.