Non-local total variation regularization models for image restoration. (April 2018)

• Record Type: Journal Article
• Authors: Jidesh, P.; K., Shivarama Holla
• Is Part Of: Computers & electrical engineering. Volume 67(2018)
• Page Start: 114
• Page End: 133
• Publication Date: 2018-04
• Subjects: Non-local total variation -- Total bounded variation -- Rayleigh noise -- Split Bergman -- Variance stabilization -- Linear blur; 68U10 -- 94A08; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.03.014
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Restoration of images corrupted by data-correlated Rayleigh noise distribution has not been studied much extensively in the literature, unlike the other noise distributions. In this paper, we analyze the degradations due to a data-correlated Rayleigh noise and a linear blurring artifact. This work employs a variance stabilization approach and two variational approaches for restoring images from their noisy and blurred observations. The split-Bregman iterative scheme is used for numerically solving the models to improve their convergence rates. Furthermore, non-local total variation and non-local total bounded variation priors are being used as regularizers in these models to improve their restoration efficiency. Various synthetic and real images (such as ultrasound and synthetic aperture radar images) are tested to show the performance of these models.