Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
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.
Cite
Text
Ahn et al. "Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01249-6_16Markdown
[Ahn et al. "Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/ahn2018eccv-fast/) doi:10.1007/978-3-030-01249-6_16BibTeX
@inproceedings{ahn2018eccv-fast,
title = {{Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network}},
author = {Ahn, Namhyuk and Kang, Byungkon and Sohn, Kyung-Ah},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01249-6_16},
url = {https://mlanthology.org/eccv/2018/ahn2018eccv-fast/}
}