ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement
This work addresses the pervasive challenge of enhancing low-light images for practical applications, offering a customizable solution, though it is incremental as it builds on existing reinforcement learning approaches.
The paper tackles the problem of low-light image enhancement by addressing variability in low-light conditions and subjective preferences, proposing ReLLIE, a deep reinforcement learning method that achieves state-of-the-art performance on various benchmarks.
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.