MATLAB Source Code Trio for Image Denoising Algorithms

In this article, I present a comprehensive MATLAB source code package integrating three distinct image denoising algorithms: BLS-GSM, BM3D, and NLM. These algorithms represent established methodologies for effective image noise reduction. The implementation provides modular functions for each algorithm, allowing users to process images through parameterized function calls. For instance, the BLS-GSM (Bayesian Least Squares - Gaussian Scale Mixture) algorithm employs wavelet transformations and statistical modeling to effectively remove Gaussian noise, with the code implementing multi-scale decomposition and Bayesian estimation procedures. The BM3D (Block-Matching 3D) algorithm utilizes collaborative filtering through block grouping and 3D transformation, preserving image details while eliminating noise particles - the source code includes robust block-matching functions and thresholding mechanisms. The NLM (Non-Local Means) algorithm leverages non-local similarity principles by calculating weighted averages of similar patches across the entire image, with the implementation efficiently handling patch comparisons and distance computations. By consolidating these three algorithms into a single codebase, users can conveniently select appropriate methods for different noise types through configurable input parameters. The modular architecture enables easy comparison of denoising performance and adaptation to specific image characteristics. This resource facilitates optimal noise reduction outcomes while maintaining computational efficiency through optimized MATLAB matrix operations and pre-allocated memory management. I hope this source code proves valuable for your image processing applications!