Search Results for author: Naishan Zheng

Found 9 papers, 1 papers with code

Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness

no code implementations4 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.

Adversarial Robustness

Empowering Low-Light Image Enhancer through Customized Learnable Priors

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.

Low-Light Image Enhancement

Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-Spectral Image Fusion

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.

Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration

no code implementations29 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.

Image Denoising Image Enhancement +4

Random Weights Networks Work as Loss Prior Constraint for Image Restoration

no code implementations29 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''.

Image Restoration Image Super-Resolution +1

Learning Sample Relationship for Exposure Correction

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.

Task 2

CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal

no code implementations6 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.

Long-range modeling Shadow Removal

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