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.

当前，低光条件给机器认知带来了重大挑战。本文提出了一种利用零参考低光增强方法改善下游任务模型性能的策略，通过利用丰富的视觉 - 语言 CLIP 先验来改进零参考低光增强方法，避免了对配对或非配对正常光数据的收集工作，并证明了不需要正常光数据的学习提示可以改善图像对比度，减少过度增强和噪声过放大。另外，通过零样本开放式词汇分类利用 CLIP 模型进行语义指导，优化低光增强以提高任务性能而非人类视觉感知。通过大量实验结果显示，所提出的方法在各种数据集上都取得了一致的改进效果，并将其与最先进的方法进行比较，展示了在各种低光数据集上的有利结果。