  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.
