Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network release_wgs7yneiajadla5vugnezulady

by Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn

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2018  

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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Type  article
Stage   submitted
Date   2018-07-25
Version   v3
Language   en ?
arXiv  1803.08664v3
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