Liu Risheng, Ma Long, Ma Tengyu, Fan Xin, Luo Zhongxuan
IEEE Trans Pattern Anal Mach Intell. 2023 May;45(5):5953-5969. doi: 10.1109/TPAMI.2022.3212995. Epub 2023 Apr 3.
Images captured from low-light scenes often suffer from severe degradations, including low visibility, color casts, intensive noises, etc. These factors not only degrade image qualities, but also affect the performance of downstream Low-Light Vision (LLV) applications. A variety of deep networks have been proposed to enhance the visual quality of low-light images. However, they mostly rely on significant architecture engineering and often suffer from the high computational burden. More importantly, it still lacks an efficient paradigm to uniformly handle various tasks in the LLV scenarios. To partially address the above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, that can address low-light enhancement task, and has the flexibility to handle other challenging downstream vision tasks. Specifically, we first establish a nested optimization formulation, together with an unrolling strategy, to explore underlying principles of a series of LLV tasks. Furthermore, we design a differentiable strategy to cooperatively search specific scene and task architectures for RUAS. Last but not least, we demonstrate how to apply RUAS for both low- and high-level LLV applications (e.g., enhancement, detection and segmentation). Extensive experiments verify the flexibility, effectiveness, and efficiency of RUAS.
从低光照场景中捕获的图像常常会遭受严重退化，包括可见度低、色偏、大量噪声等。这些因素不仅会降低图像质量，还会影响下游低光照视觉（LLV）应用的性能。人们已经提出了各种深度网络来提高低光照图像的视觉质量。然而，它们大多依赖于重大的架构工程，并且常常承受着高计算负担。更重要的是，仍然缺乏一种有效的范式来统一处理LLV场景中的各种任务。为了部分解决上述问题，我们建立了受视网膜皮层理论启发的带架构搜索的展开式（RUAS），这是一个通用学习框架，它可以解决低光照增强任务，并且具有处理其他具有挑战性的下游视觉任务的灵活性。具体而言，我们首先建立一个嵌套优化公式，连同展开策略，以探索一系列LLV任务的基本原理。此外，我们设计了一种可微策略，为RUAS协同搜索特定场景和任务架构。最后但同样重要的是，我们展示了如何将RUAS应用于低级和高级LLV应用（例如，增强、检测和分割）。大量实验验证了RUAS的灵活性、有效性和效率。
