no code implementations • ICCV 2023 • Myungsub Choi, Hana Lee, Hyong-Euk Lee
We observe that, while these degradations are hard to model using prior knowledge, they are correlated with the spatial position of the pixels within the image sensor area, and we propose a learning-based autofocus model with positional encodings (PE) to capture these patterns.
no code implementations • ICCV 2021 • Myungsub Choi, Suyoung Lee, Heewon Kim, Kyoung Mu Lee
Video frame interpolation aims to synthesize accurate intermediate frames given a low-frame-rate video.
no code implementations • ICCV 2021 • Heewon Kim, Sungyong Baik, Myungsub Choi, Janghoon Choi, Kyoung Mu Lee
Diverse user preferences over images have recently led to a great amount of interest in controlling the imagery effects for image restoration tasks.
1 code implementation • 9 Nov 2020 • Suyoung Lee, Myungsub Choi, Kyoung Mu Lee
Most conventional supervised super-resolution (SR) algorithms assume that low-resolution (LR) data is obtained by downscaling high-resolution (HR) data with a fixed known kernel, but such an assumption often does not hold in real scenarios.
Ranked #7 on Video Super-Resolution on MSU Video Upscalers: Quality Enhancement (VMAF metric)
2 code implementations • NeurIPS 2020 • Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, Kyoung Mu Lee
Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization.
1 code implementation • AAAI Conference on Artificial Intelligence 2020 • Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, Kyoung Mu Lee
Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion.
1 code implementation • CVPR 2020 • Myungsub Choi, Janghoon Choi, Sungyong Baik, Tae Hyun Kim, Kyoung Mu Lee
Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.
no code implementations • ECCV 2018 • Heewon Kim, Myungsub Choi, Bee Lim, Kyoung Mu Lee
Our framework is efficient, and it can be generalized to handle an arbitrary image resizing operation.