  Light adaptation or brightness correction is a key step in improving the
contrast and visual appeal of an image. There are multiple light-related tasks
(for example, low-light enhancement and exposure correction) and previous
studies have mainly investigated these tasks individually. However, it is
interesting to consider whether these light-related tasks can be executed by a
unified model, especially considering that our visual system adapts to external
light in such way. In this study, we propose a biologically inspired method to
handle light-related image-enhancement tasks with a unified network (called
LA-Net). First, a frequency-based decomposition module is designed to decouple
the common and characteristic sub-problems of light-related tasks into two
pathways. Then, a new module is built inspired by biological visual adaptation
to achieve unified light adaptation in the low-frequency pathway. In addition,
noise suppression or detail enhancement is achieved effectively in the
high-frequency pathway regardless of the light levels. Extensive experiments on
three tasks -- low-light enhancement, exposure correction, and tone mapping --
demonstrate that the proposed method almost obtains state-of-the-art
performance compared with recent methods designed for these individual tasks.
