NeurIPS 2019oral593 citations
Blind Super-Resolution Kernel Estimation using an Internal-GAN
Sefi Bell-Kligler, Assaf Shocher, Michal Irani
Abstract
Super resolution (SR) methods typically assume that the low-resolution (LR) image was downscaled from the unknown high-resolution (HR) image by a fixed `ideal’ downscaling kernel (e.g. Bicubic downscaling). However, this is rarely the case in real LR images, in contrast to synthetically generated SR datasets. When the assumed downscaling kernel deviates from the true one, the performance of SR methods significantly deteriorates. This gave rise to Blind-SR - namely, SR when the downscaling kernel (
BibTeX
@inproceedings{NEURIPS2019_5fd0b37c,
author = {Bell-Kligler, Sefi and Shocher, Assaf and Irani, Michal},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Blind Super-Resolution Kernel Estimation using an Internal-GAN},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/5fd0b37cd7dbbb00f97ba6ce92bf5add-Paper.pdf},
volume = {32},
year = {2019}
}