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Image/Video Restoration via Multiplanar Autoregressive Model and Low-Rank Optimization by Mading Li; Jiaying Liu; Xiaoyan Sun; Zhiwei Xiong is a Computer Science article available to read on EtoBox.

What is Image/Video Restoration via Multiplanar Autoregressive Model and Low-Rank Optimization about?

In this article, we introduce an image/video restoration approach by utilizing the high-dimensional similarity in images/videos. After grouping similar patches from neighboring frames, we propose to build a multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of the patch group, which has long been neglected by previous AR models. To further utilize the nonlocal self-similarity in images/videos, a joint multiplanar AR and low-rank based approach is proposed (MARLow) to reconstruct patch groups more effectively. Moreover, for video restoration, the temporal smoothness of the restored video is constrained by the Markov random field (MRF), where MRF encodes a priori knowledge about consistency of patches from neighboring frames. Specifically, we treat different restoration results (from different patch groups) of a certain patch as labels of an MRF, and temporal consistency among these restored patches is imposed. The proposed method is also suitable for other restoration applications such as interpolation and text removal. Extensive experimental results demonstrate that the proposed approach obtains encouraging performance comparing with state-

Who reads Image/Video Restoration via Multiplanar Autoregressive Model and Low-Rank Optimization?

Author

Mading Li; Jiaying Liu; Xiaoyan Sun; Zhiwei Xiong

Publisher

Association for Computing Machinery; Association for Computing Machinery (ACM) (ISSN 1551-6857)

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)