| Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation Y Jo, SW Oh, J Kang, SJ Kim Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 585 | 2018 |
| Investigating loss functions for extreme super-resolution Y Jo, S Yang, SJ Kim Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 57 | 2020 |
| Practical single-image super-resolution using look-up table Y Jo, SJ Kim Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 54 | 2021 |
| Tackling the ill-posedness of super-resolution through adaptive target generation Y Jo, SW Oh, P Vajda, SJ Kim Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 40 | 2021 |
| Srflow-da: Super-resolution using normalizing flow with deep convolutional block Y Jo, S Yang, SJ Kim Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 29 | 2021 |
| Deep space-time video upsampling networks J Kang, Y Jo, SW Oh, P Vajda, SJ Kim European conference on computer vision, 701-717, 2020 | 18 | 2020 |
| Learning the loss functions in a discriminative space for video restoration Y Jo, J Kang, SW Oh, S Nam, P Vajda, SJ Kim arXiv preprint arXiv:2003.09124, 2020 | 1 | 2020 |
| Supplementary: Practical Single-Image Super-Resolution Using Look-Up Table Y Jo, SJ Kim | | |
| Supplementary: Tackling the Ill-Posedness of Super-Resolution through Adaptive Target Generation Y Jo, SW Oh, P Vajda, SJ Kim | | |
