3 papers
cs.CV2025
Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing
Ruiyi Wang, Yushuo Zheng, Zicheng Zhang +4
Existing real-world image dehazing methods primarily attempt to fine-tune pre-trained models or adapt their inference procedures, thus heavily relying on the pre-trained models and…
cs.CV2024
You Only Look Around: Learning Illumination Invariant Feature for Low-light Object Detection
Mingbo Hong, Shen Cheng, Haibin Huang +2
In this paper, we introduce YOLA, a novel framework for object detection in low-light scenarios. Unlike previous works, we propose to tackle this challenging problem from the persp…
cs.CV2024
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval
Han Zhou, Wei Dong, Xiaohong Liu +4
Most existing Low-light Image Enhancement (LLIE) methods either directly map Low-Light (LL) to Normal-Light (NL) images or use semantic or illumination maps as guides. However, the…