  Low-light image enhancement aims to improve the perception of images
collected in dim environments and provide high-quality data support for image
recognition tasks. When dealing with photos captured under non-uniform
illumination, existing methods cannot adaptively extract the differentiated
luminance information, which will easily cause over-exposure and
under-exposure. From the perspective of unsupervised learning, we propose a
multi-scale attention Transformer named MSATr, which sufficiently extracts
local and global features for light balance to improve the visual quality.
Specifically, we present a multi-scale window division scheme, which uses
exponential sequences to adjust the window size of each layer. Within
different-sized windows, the self-attention computation can be refined,
ensuring the pixel-level feature processing capability of the model. For
feature interaction across windows, a global transformer branch is constructed
to provide comprehensive brightness perception and alleviate exposure problems.
Furthermore, we propose a loop training strategy, using the diverse images
generated by weighted mixing and a luminance consistency loss to improve the
model's generalization ability effectively. Extensive experiments on several
benchmark datasets quantitatively and qualitatively prove that our MSATr is
superior to state-of-the-art low-light image enhancement methods, and the
enhanced images have more natural brightness and outstanding details. The code
is released at https://github.com/fang001021/MSATr.
