BM3D Denoising Algorithms for Medical Image

Abstract

Background: Medical diagnostic and imaging technology has been fundamentally impacted by the twenty-first-century increase in internet technology, computers, wireless communication, and data storage. However, like with other imaging modalities, medical imaging may be hampered by noise and artifacts, compromising effective diagnostic analysis and potentially posing health hazards. Objective: The study focuses on the BM3D (Block Matching and 3D Filtering) technique, a state-of-the-art method, to combat the noise in medical images. By denoising these images, it aims to improve the quality of medical diagnoses and reduce associated risks. Methods: Building upon the foundation set by the Non-Local Means (NLM) filtering method, the BM3D technique utilizes a patch-based denoising mechanism. Instead of denoising individual pixels, clusters or blocks of pixels are processed collectively to improve the overall image quality. Results: BM3D will exhibit strong performance against impartial thoroughness criteria, making it a prospective stalwart in the denoising realm for medical images. Yet, certain limitations, like user-supplied noise levels and potential artifacts due to hard thresholding, are identified. Conclusion: While BM3D emerges as a powerful denoising tool for medical images, it's imperative to address its limitations to further bolster its efficacy and applicability in real-time diagnostic imaging systems.

Keywords

• bm3d
• nlm
• denoising
• gaussian noise
• algorithms