Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Abstract

Motivated by their recent advances, deep learning techniques have been widely applied to low-light image enhancement (LIE) problem. Among which, Retinex theory based ones, mostly following a decomposition-adjustment pipeline, have taken an important place due to its physical interpretation and promising performance. However, current investigations on Retinex based deep learning are still not sufficient, ignoring many useful experiences from traditional methods. Besides, the adjustment step is either performed with simple image processing techniques, or by complicated networks, both of which are unsatisfactory in practice. To address these issues, we propose a new deep learning framework for the LIE problem. The proposed framework contains a decomposition network inspired by algorithm unrolling, and adjustment networks considering both global brightness and local brightness sensitivity. By virtue of algorithm unrolling, both implicit priors learned from data and explicit priors borrowed from traditional methods can be embedded in the network, facilitate to better decomposition. Meanwhile, the consideration of global and local brightness can guide designing simple yet effective network modules for adjustment. Besides, to avoid manually parameter tuning, we also propose a self-supervised fine-tuning strategy, which can always guarantee a promising performance. Experiments on a series of typical LIE datasets demonstrated the effectiveness of the proposed method, both quantitatively and visually, as compared with existing methods.

Efficient adaptive feature aggregation network for low-light image enhancement

“…Wang et al [22] proposed DeepUPE, which combines Retinex and bilateral filtering. Liu et al [23] proposed RAUNA, which designed a decomposition network with explicit and implicit priors, and taken into account both global and local brightness in the augmented network. Some methods try to learn enhancement results from unpaired data, such as Jiang et al [24] proposed a degradation-to-refinement generation network (DRGN).…”

“…We compared some recent work on the LOL dataset, including Xu's method [21], RetinexNet [15], GLAD [13], KinD [16], LPNet [25], Zero-DCE++ [17], RUAS [18], RAUNA [23], DRGN [24]. For the KinD method, we chose an exposure of 5 as the benchmark.…”