Fuzzy-based hybrid filter for Rician noise removal. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Sharif, Muhammad; Hussain, Ayyaz; Jaffar, Muhammad; Choi, Tae-Sun
• Is Part Of: Signal, image and video processing. Volume 10:Issue 2(2016)
• Page Start: 215
• Page End: 224
• Publication Date: 2016-02
• Subjects: Magnetic resonance imaging -- Rician noise -- Fuzzy logic -- Image denoising -- Hybrid filter; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-014-0729-1
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Magnetic resonance images tend to be contaminated with random unwanted signals called noise, due to various reasons. Noise treatment of magnetic resonance brain images is considered as an important and challenging task for proper clinical and research investigations. In this manuscript, fuzzy logic-based hybrid Rician noise filter has been proposed. Proposed filtering technique uses estimated noise variance along with local and global statistics for the construction of a robust fuzzy membership function. Constructed fuzzy membership function assigns appropriate weights to the statistical estimates, based on their noise removal and detail preservation capability. Fuzzy weighted local and non-local estimators are then used for the restoration of a noisy pixel. Detailed simulations are performed, and restoration results are computed based on well-known performance measures. Numerical and visual results show that the proposed technique gives much better restored images than the existing methodologies.