  Low-light image enhancement (LLIE) is a pervasive yet challenging problem,
since: 1) low-light measurements may vary due to different imaging conditions
in practice; 2) images can be enlightened subjectively according to diverse
preferences by each individual. To tackle these two challenges, this paper
presents a novel deep reinforcement learning based method, dubbed ReLLIE, for
customized low-light enhancement. ReLLIE models LLIE as a markov decision
process, i.e., estimating the pixel-wise image-specific curves sequentially and
recurrently. Given the reward computed from a set of carefully crafted
non-reference loss functions, a lightweight network is proposed to estimate the
curves for enlightening of a low-light image input. As ReLLIE learns a policy
instead of one-one image translation, it can handle various low-light
measurements and provide customized enhanced outputs by flexibly applying the
policy different times. Furthermore, ReLLIE can enhance real-world images with
hybrid corruptions, e.g., noise, by using a plug-and-play denoiser easily.
Extensive experiments on various benchmarks demonstrate the advantages of
ReLLIE, comparing to the state-of-the-art methods.
