Learning-based video superresolution reconstruction using spatiotemporal nonlocal similarity
type: Item
instance of: scholarly article
title: Learning-based video superresolution reconstruction using spatiotemporal nonlocal similarity
zbMATH Open document ID: 1395.94046
DOI: 10.1155/2015/687074
author: Meiyu Liang
author: Junping Du
author: Ling-Hui Li
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: Aiming at improving the video visual resolution quality and details clarity, a novel learning-based video superresolution reconstruction algorithm using spatiotemporal nonlocal similarity is proposed in this paper. Objective high-resolution (HR) estimations of low-resolution (LR) video frames can be obtained by learning LR-HR correlation mapping and fusing spatiotemporal nonlocal similarities between video frames. With the objective of improving algorithm efficiency while guaranteeing superresolution quality, a novel visual saliency-based LR-HR correlation mapping strategy between LR and HR patches is proposed based on semicoupled dictionary learning. Moreover, aiming at improving performance and efficiency of spatiotemporal similarity matching and fusion, an improved spatiotemporal nonlocal fuzzy registration scheme is established using the similarity weighting strategy based on pseudo-Zernike moment feature similarity and structural similarity, and the self-adaptive regional correlation evaluation strategy. The proposed spatiotemporal fuzzy registration scheme does not rely on accurate estimation of subpixel motion, and therefore it can be adapted to complex motion patterns and is robust to noise and rotation. Experimental results demonstrate that the proposed algorithm achieves competitive superresolution quality compared to other state-of-the-art algorithms in terms of both subjective and objective evaluations.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 6926987
Wikidata QID: Q59118929
describes a project that uses: FSIM
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/687074
OpenAlex ID: W1867868138
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description: scientific article; zbMATH DE number 6926987