Collaborative brightening and amplification of low-light imagery via bi-level adversarial learning-Reference-Cited by-同舟云学术

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Collaborative brightening and amplification of low-light imagery via bi-level adversarial learning

Published:2024-10 Issue: Volume:154 Page:110558
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Gao Jiaxin^ORCID,Liu Yaohua^ORCID,Yue Ziyu,Fan Xin^ORCID,Liu Risheng^ORCID

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Liaoning Revitalization Talents Program

Publisher

Elsevier BV

Reference40 articles.

1. Deep learning for image super-resolution: A survey;Wang;IEEE Trans. Pattern Anal. Mach. Intell.,2020

2. Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method;Wang,2023

3. Low-light image and video enhancement using deep learning: A survey;Li;IEEE Trans. Pattern Anal. Mach. Intell.,2021

4. Light-guided and cross-fusion U-net for anti-illumination image super-resolution;Cheng;IEEE Trans. Circuits Syst. Video Technol.,2022

5. SurroundNet: Towards effective low-light image enhancement;Zhou;Pattern Recognit.,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Dual-Stream-Modulated Learning Framework for Illuminating and Super-Resolving Ultra-Dark Images;IEEE Transactions on Neural Networks and Learning Systems;2024

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