3 papers
cs.CV2025
PromptLNet: Region-Adaptive Aesthetic Enhancement via Prompt Guidance in Low-Light Enhancement Net
Jun Yin, Yangfan He, Miao Zhang +4
Learning and improving large language models through human preference feedback has become a mainstream approach, but it has rarely been applied to the field of low-light image enha…
cs.CV2025
TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement
Miao Zhang, Jun Yin, Pengyu Zeng +3
Deep learning-based image enhancement methods show significant advantages in reducing noise and improving visibility in low-light conditions. These methods are typically based on o…
cs.CV2025
Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion
Yangfan He, Sida Li, Jianhui Wang +11
Recent advancements in text-to-image (T2I) generation using diffusion models have enabled cost-effective video-editing applications by leveraging pre-trained models, eliminating th…