Low-light image enhancement plays very important roles in low-level vision
field. Recent works have built a large variety of deep learning models to
address this task. However, these approaches mostly rely on significant
architecture engineering and suffer from high computational burden. In this
paper, we propose a new method, named Retinex-inspired Unrolling with
Architecture Search (RUAS), to construct lightweight yet effective enhancement
network for low-light images in real-world scenario. Specifically, building
upon Retinex rule, RUAS first establishes models to characterize the intrinsic
underexposed structure of low-light images and unroll their optimization
processes to construct our holistic propagation structure. Then by designing a
cooperative reference-free learning strategy to discover low-light prior
architectures from a compact search space, RUAS is able to obtain a
top-performing image enhancement network, which is with fast speed and requires
few computational resources. Extensive experiments verify the superiority of
our RUAS framework against recently proposed state-of-the-art methods.

本文提出了一种新的方法，名为 Retinex-inspired Unrolling with Architecture Search（RUAS），用于构建用于真实世界场景下低光图像的轻量级但有效的增强网络，该方法基于 Retinex 规则，通过设计协作的无参考学习策略来发现来自紧凑搜索空间的低光先验架构，展开其优化过程来构建全面的传播结构，证实了该方法优于最新方法。