no code implementations • 5 May 2024 • Xiaole Tang, Xin Hu, Xiang Gu, Jian Sun
In this work, we propose a novel Residual-Conditioned Optimal Transport (RCOT) approach, which models image restoration as an optimal transport (OT) problem for both unpaired and paired settings, introducing the transport residual as a unique degradation-specific cue for both the transport cost and the transport map.
no code implementations • CVPR 2023 • Xiaole Tang, XiLe Zhao, Jun Liu, Jianli Wang, Yuchun Miao, Tieyong Zeng
To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios.