4 papers
Continuous Splatting meets Retinex: Continuous Gaussian Splatting and Implicit Reflectance Modeling for Low-Light Image Enhancement
Yuhan Chen, Yicui Shi, Guofa Li +5
Low-light image enhancement aims to recover clear images from low-illumination observations and is crucial for high-level downstream vision tasks. However, existing methods frequen…
Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices
Guangrui Bai, Hailong Yan, Wenhai Liu +2
Real-time low-light image enhancement on mobile and embedded devices requires models that balance visual quality and computational efficiency. Existing deep learning methods often…
Rethinking Low-Light Image Enhancement: A Log-Domain Intensity--Chromaticity Decoupling Perspective
Guangrui Bai, Yifan Mei, Yahui Deng +4
Explicit reconstruction constraints derived from the decoupled representation are further imposed to suppress abnormal channel amplification and chromatic noise. Experiments on LOL…
UltraFast-LiNET: Light-weight multi-scale shift convolutional network for real-time low-light image enhancement
Yuhan Chen, Yicui Shi, Guofa Li +7
Addressing the urgent need for high-performance real-time low-light image enhancement on resource-constrained edge devices in low-illumination scenarios such as nighttime and tunne…