Image Enhancement Comparison Hub
4 papers - avg viability 5.8
Top Papers
- Retinex Meets Language: A Physics-Semantics-Guided Underwater Image Enhancement Network(7.0)
Enhance underwater images by combining physics-based correction with CLIP-guided semantic restoration, leveraging a newly created large-scale image-text dataset.
- Anchor then Polish for Low-light Enhancement(7.0)
A novel anchor-then-polish framework for superior low-light image enhancement.
- Low-light Image Enhancement with Retinex Decomposition in Latent Space(6.0)
A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques.
- Empowering Semantic-Sensitive Underwater Image Enhancement with VLM(3.0)
A novel mechanism using Vision-Language Models to enhance underwater image quality by focusing on semantic-sensitive regions.