Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement
<|reference_start|>Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement: Currently, low-light conditions present a significant challenge for machine cognition. In this paper, rather than optimizing models by assuming that human and machine cognition are correlated, we use zero-reference low-light enhancement to improve the performance of downstream task models. We propose to improve the zero-reference low-light enhancement method by leveraging the rich visual-linguistic CLIP prior without any need for paired or unpaired normal-light data, which is laborious and difficult to collect. We propose a simple but effective strategy to learn prompts that help guide the enhancement method and experimentally show that the prompts learned without any need for normal-light data improve image contrast, reduce over-enhancement, and reduce noise over-amplification. Next, we propose to reuse the CLIP model for semantic guidance via zero-shot open vocabulary classification to optimize low-light enhancement for task-based performance rather than human visual perception. We conduct extensive experimental results showing that the proposed method leads to consistent improvements across various datasets regarding task-based performance and compare our method against state-of-the-art methods, showing favorable results across various low-light datasets.<|reference_end|>
@article{morawski2024unsupervised,
    title={Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance
  for Low-Light Image Enhancement},
    author={Igor Morawski, Kai He, Shusil Dangi, Winston H. Hsu},
    journal={arXiv preprint arXiv:2405.11478},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11478},
    primaryClass={cs.CV eess.IV}
}