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S2A: Scale-Attention-Aware Networks for Video Super-Resolution
Convolutional Neural Networks (CNNs) have been widely used in video super-resolution (VSR). Most existing VSR methods focus on how to utilize the information of multiple frames, while neglecting the feature correlations of the intermediate features, thus limiting the feature expression of the models...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8619237/ https://www.ncbi.nlm.nih.gov/pubmed/34828096 http://dx.doi.org/10.3390/e23111398 |
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author | Guo, Taian Dai, Tao Liu, Ling Zhu, Zexuan Xia, Shu-Tao |
author_facet | Guo, Taian Dai, Tao Liu, Ling Zhu, Zexuan Xia, Shu-Tao |
author_sort | Guo, Taian |
collection | PubMed |
description | Convolutional Neural Networks (CNNs) have been widely used in video super-resolution (VSR). Most existing VSR methods focus on how to utilize the information of multiple frames, while neglecting the feature correlations of the intermediate features, thus limiting the feature expression of the models. To address this problem, we propose a novel SAA network, that is, Scale-and-Attention-Aware Networks, to apply different attention to different temporal-length streams, while further exploring both spatial and channel attention on separate streams with a newly proposed Criss-Cross Channel Attention Module ([Formula: see text]). Experiments on public VSR datasets demonstrate the superiority of our method over other state-of-the-art methods in terms of both quantitative and qualitative metrics. |
format | Online Article Text |
id | pubmed-8619237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86192372021-11-27 S2A: Scale-Attention-Aware Networks for Video Super-Resolution Guo, Taian Dai, Tao Liu, Ling Zhu, Zexuan Xia, Shu-Tao Entropy (Basel) Article Convolutional Neural Networks (CNNs) have been widely used in video super-resolution (VSR). Most existing VSR methods focus on how to utilize the information of multiple frames, while neglecting the feature correlations of the intermediate features, thus limiting the feature expression of the models. To address this problem, we propose a novel SAA network, that is, Scale-and-Attention-Aware Networks, to apply different attention to different temporal-length streams, while further exploring both spatial and channel attention on separate streams with a newly proposed Criss-Cross Channel Attention Module ([Formula: see text]). Experiments on public VSR datasets demonstrate the superiority of our method over other state-of-the-art methods in terms of both quantitative and qualitative metrics. MDPI 2021-10-25 /pmc/articles/PMC8619237/ /pubmed/34828096 http://dx.doi.org/10.3390/e23111398 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guo, Taian Dai, Tao Liu, Ling Zhu, Zexuan Xia, Shu-Tao S2A: Scale-Attention-Aware Networks for Video Super-Resolution |
title | S2A: Scale-Attention-Aware Networks for Video Super-Resolution |
title_full | S2A: Scale-Attention-Aware Networks for Video Super-Resolution |
title_fullStr | S2A: Scale-Attention-Aware Networks for Video Super-Resolution |
title_full_unstemmed | S2A: Scale-Attention-Aware Networks for Video Super-Resolution |
title_short | S2A: Scale-Attention-Aware Networks for Video Super-Resolution |
title_sort | s2a: scale-attention-aware networks for video super-resolution |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8619237/ https://www.ncbi.nlm.nih.gov/pubmed/34828096 http://dx.doi.org/10.3390/e23111398 |
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