EPLL image restoration with a bounded asymmetrical Student's-t mixture model

Yu Q.; Cao G.; Shi H.Fu P.Zhang Y.

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EPLL image restoration with a bounded asymmetrical Student's-t mixture model

机译：EPLL image restoration with a bounded asymmetrical Student's-t mixture model

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? 2022 Elsevier Inc.The expected patch log-likelihood (EPLL) model is a patch prior-based image restoration method which received extensive attention in image processing in recent years for its outstanding ability to preserve the detail and structure. However, due to using the Gaussian mixture model (GMM) with the noise sensitivity as the local prior, the EPLL model suffers from undesired artifact and poor robustness frequently. In this paper, to restrain the generation of artifact of EPLL model, we replace the GMM with a bounded asymmetrical Student's-t mixture model (BASMM), which is sufficiently flexible to fit different shapes of image data, such as non-Gaussian, non-symmetric, and bounded support data. Then, the anisotropic nonlocal self-similarity (ANSS) based regularization parameters are designed to improve the robustness of the proposed model. Experimental results demonstrate the competitiveness of our proposed model compared with that of state-of-the-art methods in performance both visually and quantitatively.

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《Journal of visual communication & image representation》 |2022年第10期|103611.1-103611.18|共18页
作者
Yu Q.; Cao G.; Shi H.Fu P.Zhang Y.;
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School of Computer Science and Engineering Nanjing University of Science and Technology;

Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and Telecommunications||School of Internet of Things Nanjing University of Posts and Telecommunications;

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Anisotropic nonlocal self-similarity; Bounded asymmetrical Student's-t mixture model; EPLL image restoration; Finite mixture model; Regularization parameters;

EPLL image restoration with a bounded asymmetrical Student's-t mixture model

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