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Fast, accurate, and lightweight super-resolution with cascading residual network
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Publication Year
2018-01-01
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer Verlag
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.11214 LNCS, pp.256-272
Keyword
Deep convolutional neural networkSuper-resolution
Mesh Keyword
Deep convolutional neural networksDeep networksImage super resolutionsLearning methodsSingle imagesState-of-the-art methodsSuper resolutionVariant models
All Science Classification Codes (ASJC)
Theoretical Computer ScienceComputer Science (all)
Abstract
In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36240
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055133310&origin=inward
DOI
https://doi.org/10.1007/978-3-030-01249-6_16
Journal URL
https://www.springer.com/series/558
Type
Conference
Funding
This research was supported through the National Research Foundation of Korea (NRF) funded by the Ministry of Education: NRF-2016R1D1A1B03933875 (K.-A. Sohn) and NRF-2016R1A6A3A11932796 (B. Kang).
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Sohn, Kyung-Ah Image
Sohn, Kyung-Ah손경아
Department of Software and Computer Engineering
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