Recently, there has been a growing interest in constructing deep learning schemes for Low-Light Vision (LLV). Existing techniques primarily focus on designing task-specific and data-dependent vision models on the standard RGB domain, which inherently contain latent data associations. In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain. This novel approach connects diverse vision problems by explicitly depicting data generation, which is the first in the field. To precisely characterize the latent correspondence between the generative procedure and the vision task, we establish a bilevel model with the parameters of the generative block defined as the upper level and the parameters of the vision task defined as the lower level. We further develop two types of learning strategies targeting different goals, namely low cost and high accuracy, to acquire a new bilevel generative learning paradigm. The generative blocks embrace a strong generalization ability in other low-light vision tasks through the bilevel optimization on enhancement tasks. Extensive experimental evaluations on three representative low-light vision tasks, namely enhancement, detection, and segmentation, fully demonstrate the superiority of our proposed approach. The code will be available at https://github.com/Yingchi1998/BGL.

翻译：近年来，构建面向低光照视觉（Low-Light Vision, LLV）的深度学习方案日益受到关注。现有技术主要集中在标准RGB域上设计任务特定且数据依赖的视觉模型，这些模型天然包含潜在的数据关联。本研究通过引入生成模块将数据从RAW域转换至RGB域，提出一种通用的低光照视觉解决方案。该创新方法通过显式描述数据生成过程将不同的视觉问题关联起来，这在领域内尚属首次。为精确刻画生成过程与视觉任务之间的潜在对应关系，我们建立了双层模型：将生成模块的参数定义为上层变量，视觉任务的参数定义为下层变量。针对低成本与高精度两种不同目标，我们进一步开发了两类学习策略，从而构建出全新的双层生成式学习范式。基于增强任务的双层优化，生成模块在其他低光照视觉任务中展现出强大的泛化能力。在增强、检测与分割三个代表性低光照视觉任务上的大量实验评估充分证明了所提方法的优越性。代码将发布在https://github.com/Yingchi1998/BGL。