  Low-light images often suffer from limited visibility and multiple types of
degradation, rendering low-light image enhancement (LIE) a non-trivial task.
Some endeavors have been recently made to enhance low-light images using
convolutional neural networks (CNNs). However, they have low efficiency in
learning the structural information and diverse illumination levels at the
local regions of an image. Consequently, the enhanced results are affected by
unexpected artifacts, such as unbalanced exposure, blur, and color bias. To
this end, this paper proposes a novel framework, called ClassLIE, that combines
the potential of CNNs and transformers. It classifies and adaptively learns the
structural and illumination information from the low-light images in a holistic
and regional manner, thus showing better enhancement performance. Our framework
first employs a structure and illumination classification (SIC) module to learn
the degradation information adaptively. In SIC, we decompose an input image
into an illumination map and a reflectance map. A class prediction block is
then designed to classify the degradation information by calculating the
structure similarity scores on the reflectance map and mean square error on the
illumination map. As such, each input image can be divided into patches with
three enhancement difficulty levels. Then, a feature learning and fusion (FLF)
module is proposed to adaptively learn the feature information with CNNs for
different enhancement difficulty levels while learning the long-range
dependencies for the patches in a holistic manner. Experiments on five
benchmark datasets consistently show our ClassLIE achieves new state-of-the-art
performance, with 25.74 PSNR and 0.92 SSIM on the LOL dataset.
