We set a new benchmark for single-image super-resolution by exploiting progressiveness both in architecture and training. The proposed multi-scale models, ProSR and ProSRGan, improve the reconstruction quality in terms of PSNR and visual quality respectively. ProSR is one of the winning teams.
MsLapSRN: Lai, Wei-Sheng, et al. “Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks.” arXiv preprint arXiv:1710.01992 (2017).
EDSR, MDSR: Lim, Bee, et al. “Enhanced deep residual networks for single image super-resolution.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Vol. 1. No. 2. 2017.
RDN:Zhang, Yulun, et al. “Residual Dense Network for Image Super-Resolution.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
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