no code implementations • 18 Mar 2024 • Howard Zhang, Yunhao Ba, Ethan Yang, Rishi Upadhyay, Alex Wong, Achuta Kadambi, Yun Guo, Xueyao Xiao, Xiaoxiong Wang, Yi Li, Yi Chang, Luxin Yan, Chaochao Zheng, Luping Wang, Bin Liu, Sunder Ali Khowaja, Jiseok Yoon, Ik-Hyun Lee, Zhao Zhang, Yanyan Wei, Jiahuan Ren, Suiyi Zhao, Huan Zheng
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
no code implementations • 20 Nov 2023 • Yanyan Wei, Zhao Zhang, Jiahuan Ren, Xiaogang Xu, Richang Hong, Yi Yang, Shuicheng Yan, Meng Wang
The generalization capability of existing image restoration and enhancement (IRE) methods is constrained by the limited pre-trained datasets, making it difficult to handle agnostic inputs such as different degradation levels and scenarios beyond their design scopes.
no code implementations • 2 Oct 2022 • Jiahuan Ren, Zhao Zhang, Richang Hong, Mingliang Xu, Yi Yang, Shuicheng Yan
Low-light image enhancement (LLIE) aims at improving the illumination and visibility of dark images with lighting noise.
no code implementations • 24 Feb 2021 • Jiahuan Ren, Qing Liao, Xuekai Ma, Stefan Schumacher, Jiannian Yao, Hongbing Fu
The ability to control the spin-orbit interaction of light in optical microresonators is of fundamental importance for future photonics.
Optics Mesoscale and Nanoscale Physics
no code implementations • 26 Dec 2019 • Jiahuan Ren, Zhao Zhang, Sheng Li, Yang Wang, Guangcan Liu, Shuicheng Yan, Meng Wang
Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.
no code implementations • 4 Aug 2019 • Zhao Zhang, Jiahuan Ren, Sheng Li, Richang Hong, Zheng-Jun Zha, Meng Wang
Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.
no code implementations • 11 Jun 2019 • Zhao Zhang, Jiahuan Ren, Weiming Jiang, Zheng Zhang, Richang Hong, Shuicheng Yan, Meng Wang
We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL).