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.

本研究提出了一种生物启发的方法来处理光相关的图像增强任务，使用统一的网络（称为 LA-Net）并设计了一个基于频率的分解模块来实现统一的光适应，同时在高频通道中实现了噪声抑制或细节增强。对低光增强、曝光校正和色调映射这三个任务进行了广泛实验，并证明了所提出方法在这些独立任务中获得了接近最先进技术的表现。