  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.
