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

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

23 Mar 2018ECCV 2018 9arXiv:1803.08664archive 2025-07-28

Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn

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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