MATLAB Implementation of BM3D Denoising Algorithm

This article presents the source code implementation of the BM3D denoising method. BM3D is a block-matching based 3D filtering algorithm designed for image noise reduction. The algorithm operates through two primary stages: The first stage involves partitioning the image into overlapping blocks and performing similarity-based block matching to form 3D groups of similar patches. The second stage employs collaborative filtering in the transform domain, where a 3D transform is applied to each group, followed by thresholding or Wiener filtering in the frequency domain. Our MATLAB implementation includes comprehensive code comments that clarify key algorithmic components such as block partitioning strategies, similarity thresholding mechanisms, and transform domain operations. The code structure demonstrates practical implementation details including patch extraction functions, distance calculation methods for block matching, and inverse transformation procedures. Additionally, we provide guidance on applying the BM3D algorithm to various image types with parameter optimization techniques for achieving optimal denoising performance. The implementation covers noise estimation methods and adaptive parameter selection based on noise characteristics. This article aims to deliver valuable technical insights for researchers and practitioners working on image restoration applications.