Image denoising via patch-based adaptive Gaussian mixture prior method. Issue 6 (September 2016)

• Record Type: Journal Article
• Authors: Cai, Nian; Zhou, Yang; Wang, Shengru; Ling, Bingo; Weng, Shaowei
• Is Part Of: Signal, image and video processing. Volume 10:Issue 6(2016)
• Page Start: 993
• Page End: 999
• Publication Date: 2016-09
• Subjects: Gaussian mixture models -- Image denoising -- Expected patch log likelihood -- Image patch priors -- Maximum a posteriori; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0850-9
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Although the expected patch log likelihood (EPLL) achieves good performance for denoising, an inherent nonadaptive problem exists. To solve this problem, an adaptive learning is introduced into the EPLL in this paper. Inspired from the structured sparse dictionary, an adaptive Gaussian mixture model (GMM) is proposed based on patch priors. The maximum a posteriori estimation is employed to cluster and update the image patches. Also, the new image patches are used to update the GMM. We perform these two steps alternately until the desired denoised results are achieved. Experimental results show that the proposed denoising method outperforms the existing image denoising algorithms.