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Title: Image restoration via simultaneous nonlocal self-similarity priors
Authors: Zha, Zhiyuan
Yuan, Xin
Zhou, Jiantao
Zhu, Ce
Wen, Bihan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Zha, Z., Yuan, X., Zhou, J., Zhu, C. & Wen, B. (2020). Image restoration via simultaneous nonlocal self-similarity priors. IEEE Transactions On Image Processing, 29, 8561-8576.
Journal: IEEE Transactions on Image Processing
Abstract: Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar patches to construct patch groups, recent studies have revealed that structural sparse representation (SSR) models can achieve promising performance in various image restoration tasks. However, most existing SSR methods only exploit the NSS prior from the input degraded (internal) image, and few methods utilize the NSS prior from external clean image corpus; how to jointly exploit the NSS priors of internal image and external clean image corpus is still an open problem. In this article, we propose a novel approach for image restoration by simultaneously considering internal and external nonlocal self-similarity (SNSS) priors that offer mutually complementary information. Specifically, we first group nonlocal similar patches from images of a training corpus. Then a group-based Gaussian mixture model (GMM) learning algorithm is applied to learn an external NSS prior. We exploit the SSR model by integrating the NSS priors of both internal and external image data. An alternating minimization with an adaptive parameter adjusting strategy is developed to solve the proposed SNSS-based image restoration problems, which makes the entire algorithm more stable and practical. Experimental results on three image restoration applications, namely image denoising, deblocking and deblurring, demonstrate that the proposed SNSS produces superior results compared to many popular or state-of-the-art methods in both objective and perceptual quality measurements.
ISSN: 1057-7149
DOI: 10.1109/TIP.2020.3015545
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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