A codebook-driven approach for low-light image enhancement
- 30 May 2025
- journal article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 156
Abstract
No abstract availableAll Related Versions
Funding Information
- National Natural Science Foundation of China (62272319)
- National Natural Science Foundation of China (62476175)
- National Natural Science Foundation of China (62476172)
- National Natural Science Foundation of China (82261138629)
- Basic and Applied Basic Research Foundation of Guangdong Province (2023B1212060076)
- Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515011637)
- Basic and Applied Basic Research Foundation of Guangdong Province (2023A1515010677)
- Shenzhen Science and Technology Innovation Program (JCYJ20220818095803007)
- Shenzhen Science and Technology Innovation Program (JCYJ20210324094413037)
- Shenzhen Science and Technology Innovation Program (JCYJ20240813142206009)
This publication has 29 references indexed in Scilit:
- Semantic-aware enhancement: Integrating semantic compensation with 3-Dimensional Lookup Tables for low-light image enhancementEngineering Applications of Artificial Intelligence, 2023
- DA-DRN: A degradation-aware deep Retinex network for low-light image enhancementDigital Signal Processing, 2023
- A lightweight RGB superposition effect adjustment network for low-light image enhancement and denoisingEngineering Applications of Artificial Intelligence, 2023
- Global-and-local aware network for low-light image enhancementEngineering Applications of Artificial Intelligence, 2023
- Zero-referenced low-light image enhancement with adaptive filter networkEngineering Applications of Artificial Intelligence, 2023
- R2RNet: Low-light image enhancement via Real-low to Real-normal NetworkJournal of Visual Communication and Image Representation, 2022
- Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation DatasetInternational Journal of Computer Vision, 2021
- Getting to know low-light images with the Exclusively Dark datasetComputer Vision and Image Understanding, 2019
- LLNet: A deep autoencoder approach to natural low-light image enhancementPattern Recognition, 2017
- Multi-metric learning for multi-sensor fusion based classificationInformation Fusion, 2013