An iterative denoising system based on Wiener filtering with application to biomedical images. (1st May 2017)

• Record Type: Journal Article
• Authors: Lahmiri, Salim
• Is Part Of: Optics & laser technology. Volume 90(2017)
• Page Start: 128
• Page End: 132
• Publication Date: 2017-05-01
• Subjects: Biomedical images -- Denoising -- Wiener filter -- Iterative system -- Peak-signal-to-noise-ratio; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2016.11.015
• Languages: English
• ISSNs: 0030-3992

Abstract

Abstract: Biomedical image denoising systems are important for accurate clinical diagnosis. The purpose of this study is to present a simple and effective iterative multistep image denoising system based on Wiener filtering (WF) where the denoised image from one stage is the input to the next stage. The denoising process stops when a particular condition measured by image energy is adaptively achieved. The proposed iterative system is tested on real clinical images and performance is measured by the well-known peak-signal-to-noise-ratio (PSNR) statistic. Experimental results showed that the proposed iterative system outperforms conventional image denoising algorithms; including wavelet packet (WP), fourth order partial differential equation (FOPDE), nonlocal Euclidean means (NLEM), first order local statistics (FOLS), and single Wiener filter used as baseline model. The experimental results demonstrate that the proposed approach can remove noise automatically and effectively while edges and texture characteristics are preserved. Highlights: A simple and effective iterative multistep image denoising system based on Wiener filtering (WF) is presented. The proposed iterative system is tested on real clinical images. Performance is measured by the well-known peak-signal-to-noise-ratio (PSNR) statistic. Proposed iterative system outperforms several common denoising techniques. Proposed iterative system can remove noise automatically and effectively.