no code implementations • 19 Apr 2024 • JunMing Hou, ZiHan Cao, Naishan Zheng, Xuan Li, Xiaoyu Chen, Xinyang Liu, Xiaofeng Cong, Man Zhou, Danfeng Hong
In this way, our proposed method is capable of benefiting the cascaded modeling rule while achieving favorable performance in the efficient manner.
no code implementations • 4 Dec 2023 • Guanlin Li, Naishan Zheng, Man Zhou, Jie Zhang, Tianwei Zhang
However, these works lack analysis of adversarial information or perturbation, which cannot reveal the mystery of adversarial examples and lose proper interpretation.
1 code implementation • ICCV 2023 • Naishan Zheng, Man Zhou, Yanmeng Dong, Xiangyu Rui, Jie Huang, Chongyi Li, Feng Zhao
In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm.
no code implementations • ICCV 2023 • Man Zhou, Jie Huang, Naishan Zheng, Chongyi Li
Such designs penetrate the image reasoning prior into deep unfolding networks while improving its interpretability and representation capability.
no code implementations • 29 Mar 2023 • Man Zhou, Naishan Zheng, Jie Huang, Xiangyu Rui, Chunle Guo, Deyu Meng, Chongyi Li, Jinwei Gu
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''.
no code implementations • 29 Mar 2023 • Man Zhou, Naishan Zheng, Jie Huang, Chunle Guo, Chongyi Li
We investigate the efficacy of our belief from three perspectives: 1) from task-customized MAE to native MAE, 2) from image task to video task, and 3) from transformer structure to convolution neural network structure.
no code implementations • ICCV 2023 • Qi Zhu, Man Zhou, Naishan Zheng, Chongyi Li, Jie Huang, Feng Zhao
Video deblurring aims to restore the latent video frames from their blurred counterparts.
no code implementations • CVPR 2023 • Jie Huang, Feng Zhao, Man Zhou, Jie Xiao, Naishan Zheng, Kaiwen Zheng, Zhiwei Xiong
Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network.
1 code implementation • 6 Sep 2022 • Qianhao Yu, Naishan Zheng, Jie Huang, Feng Zhao
The key to shadow removal is recovering the contents of the shadow regions with the guidance of the non-shadow regions.
no code implementations • 15 Jul 2022 • Naishan Zheng, Jie Huang, Qi Zhu, Man Zhou, Feng Zhao, Zheng-Jun Zha
Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic.