2 code implementations • 17 Aug 2023 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model.
no code implementations • 24 Jun 2022 • Kai Xing, Shang Li, Xiaoguang Yang
Using an unbalanced panel data covering 75 countries from 1991 to 2019, we explore how the political risk impacts on food reserve ratio.
1 code implementation • CVPR 2022 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
Compared with previous deterministic degradation models, PDM could model more diverse degradations and generate HR-LR pairs that may better cover the various degradations of test images, and thus prevent the SR model from over-fitting to specific ones.
no code implementations • 9 Nov 2021 • Shang Li, GuiXuan Zhang, Zhengxiong Luo, Jie Liu, Zhi Zeng, Shuwu Zhang
In this paper, instead of directly applying the LR guidance, we propose an additional invertible flow guidance module (FGM), which can transform the downscaled representation to the visually plausible image during downscaling and transform it back during upscaling.
no code implementations • 6 Jul 2021 • Shang Li, GuiXuan Zhang, Zhengxiong Luo, Jie Liu, Zhi Zeng, Shuwu Zhang
As a result, most previous methods may suffer a performance drop when the degradations of test images are unknown and various (i. e. the case of blind SR).
1 code implementation • 14 May 2021 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
Ranked #2 on Blind Super-Resolution on DIV2KRK - 4x upscaling
1 code implementation • NeurIPS 2020 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
Ranked #2 on Blind Super-Resolution on Set5 - 2x upscaling