| Autor: |
Prasad, Preena, Anitha, J. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3168 Issue 1, p1-10, 10p |
| Abstrakt: |
Medical imaging's diagnostic accuracy hinges on clear images, often compromised by noise. Up to now researchers have introduced a variety of noise reduction methods, each carrying its own advantages and drawbacks. This study conducts an in-depth analysis of Non-Local Means (NLM) denoising for diverse noise levels in medical images. Medical images distorted by various levels of noise are subjected to NLM denoising using fixed patch and window size along with a randomized 'h' parameter. The investigation aims to uncover the denoising process's impact on image quality across different noise levels. Performance evaluation relies on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, providing quantitative insight into denoising efficacy and structural preservation. By systematically evaluating the denoising outcomes, this investigation contributes to understanding the potential of NLM denoising for enhancing image quality under diverse noise conditions. This work underscores NLM denoising's potential to elevate medical image quality across a spectrum of noise scenarios, advancing the field of medical image processing. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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