2021 CVPR Workshops SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block Jo, Younghyun, Yang, Sejong, and Kim, Seon Joo In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021 PDF Code CVPR Practical Single-Image Super-Resolution Using Look-Up Table Jo, Younghyun, and Kim, Seon Joo In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021 PDF Supp Code CVPR Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target Generation Jo, Younghyun, Oh, Seoung Wug, Vajda, Peter, and Kim, Seon Joo In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021 PDF Supp Code 2020 ECCV Deep Space-Time Video Upsampling Networks Kang, Jaeyeon, Jo, Younghyun, Oh, Seoung Wug, Vajda, Peter, and Kim, Seon Joo In The European Conference on Computer Vision (ECCV) 2020 PDF Supp Code Demo CVPR Workshops Investigating loss functions for extreme super-resolution Jo, Younghyun, Yang, Sejong, and Kim, Seon Joo In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020 PDF Code arXiv Learning the Loss Functions in a Discriminative Space for Video Restoration Jo, Younghyun, Kang, Jaeyeon, Oh, Seoung Wug, Nam, Seonghyeon, Vajda, Peter, and Kim, Seon Joo arXiv preprint arXiv:2003.09124 2020 arXiv 2018 CVPR Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation Jo, Younghyun, Oh, Seoung Wug, Kang, Jaeyeon, and Kim, Seon Joo In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2018 PDF Code Demo