论文标题
自我对准的凹面曲线：无监督适应的照明增强
Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation
论文作者
论文摘要
低光条件不仅会降低人类的视觉体验，而且还会降低下游机器分析的性能。尽管许多作品是为低光增强或域自适应机分析而设计的，但前者的考虑较少，而后者却忽略了图像级信号调节的潜力。如何从机器视觉的角度恢复未充分的图像/视频。在本文中，我们是第一个为高级视觉提出可学习的照明增强模型的人。受到真实摄像机响应功能的启发，我们假设照明增强功能应该是凹面曲线，并建议通过离散的积分来满足这种凹度。为了从机器视觉的角度调整照明而没有特定于任务的注释数据，我们设计了一种不对称的跨域自我监督训练策略。我们的模型架构和培训设计相互受益，形成了强大的无监督的正常光适应框架。全面的实验表明，我们的方法超过了现有的低光增强和适应方法，并在各种低光视觉任务上显示出卓越的概括，包括分类，检测，动作识别和光流估计。项目网站：https：//daooshee.github.io/sacc-website/
Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. Project website: https://daooshee.github.io/SACC-Website/
