CRISP: Progressive Illumination Curriculum for Recurrent Low-Light Video Enhancement with Semantic Priors
Low-light video enhancement requires the recovery of brightness, color, and fine spatial structures while preserving temporal coherence in the presence of noise and motion.
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Key points
- This paper presents CRISP, a recurrent framework trained using a progressive illumination curriculum.
- Its two-frame recurrent architecture propagates information across successive frames to exploit temporal context beyond the immediate input pair.
- This curriculum stabilizes recurrent training without introducing additional inference cost and yields the largest performance improvement among the components evaluated in the ablation study.
- To further support structural reconstruction, the framework optionally incorporates frozen DINOv3 semantic features through adaptive layer normalization (AdaLN)-style modulation, without fine-tuning the foundation model.
Sources (1)
- [1]CRISP: Progressive Illumination Curriculum for Recurrent Low-Light Video Enhancement with Semantic PriorsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 10:51 PM
Low-light video enhancement requires the recovery of brightness, color, and fine spatial structures while preserving temporal coherence in the presence of noise and motion.
This paper presents CRISP, a recurrent framework trained using a progressive illumination curriculum.
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