Enlightening Low-Light Images With Dynamic Guidance for Context Enrichment

Author:

Zhu Lingyu1ORCID,Yang Wenhan2,Chen Baoliang1ORCID,Lu Fangbo3,Wang Shiqi1ORCID

Affiliation:

1. Department of Computer Science, City University of Hong Kong, Hong Kong

2. School of EEE, Nanyang Technological University, Singapore

3. KingsoftCloud, Beijing, China

Funder

Shenzhen Virtual University Park, Science Technology and Innovation Committee of Shenzhen Municipality

National Natural Science Foundation of China

Hong Kong Research Grants Council, Early Career Scheme

General Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Cited by 13 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. Research on Unsupervised Low-Light Railway Fastener Image Enhancement Method Based on Contrastive Learning GAN;Sensors;2024-06-11

2. Temporally Consistent Enhancement of Low-Light Videos via Spatial-Temporal Compatible Learning;International Journal of Computer Vision;2024-05-23

3. Gradient and Brightness Guided Low-Light Enhancement with Attention-Based Self-Paced Learning;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

4. Difficulty-aware Dynamic Network for Lightweight Exposure Correction;IEEE Transactions on Circuits and Systems for Video Technology;2024

5. Pseudo-Supervised Low-Light Image Enhancement With Mutual Learning;IEEE Transactions on Circuits and Systems for Video Technology;2024-01

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