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Erschienen in:
Bayesian Semantic Instance Segmentation in Open Set World Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network

verfasst von : Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
Nächstes Kapitel ExFuse: Enhancing Feature Fusion for Semantic Segmentation
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Metadaten
Titel
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
verfasst von
Namhyuk Ahn
Byungkon Kang
Kyung-Ah Sohn
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01248-9

Electronic ISBN: 978-3-030-01249-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01249-6_16

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