Abstract
Images captured from low-light scenes often suffer from severe degradations, including low visibility, color cast and intensive noises, etc. These factors not only affect image qualities, but also degrade the performance of downstream Low-Light Vision (LLV) applications. A variety of deep learning methods have been proposed to enhance the visual quality of low-light images. However, these approaches mostly rely on significant architecture engineering to obtain proper low-light models and often suffer from high computational burden. Furthermore, it is still challenging to extend these enhancement techniques to handle other LLVs. To partially address above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, which not only can address low-light enhancement task, but also has the flexibility to handle other more challenging downstream vision applications. 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 construct 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.
Abstract (translated)
URL
https://arxiv.org/abs/2112.04719
