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

Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
2018 Lecture Notes in Computer Science  
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 realworld 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
more » ... iant 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.
doi:10.1007/978-3-030-01249-6_16 fatcat:4ibhd6um5jahji3czpqsss2apy