Image captured under low-light conditions presents unpleasing artifacts,
which debilitate the performance of feature extraction for many upstream visual
tasks. Low-light image enhancement aims at improving brightness and contrast,
and further reducing noise that corrupts the visual quality. Recently, many
image restoration methods based on Swin Transformer have been proposed and
achieve impressive performance. However, On one hand, trivially employing Swin
Transformer for low-light image enhancement would expose some artifacts,
including over-exposure, brightness imbalance and noise corruption, etc. On the
other hand, it is impractical to capture image pairs of low-light images and
corresponding ground-truth, i.e. well-exposed image in same visual scene. In
this paper, we propose a dual-branch network based on Swin Transformer, guided
by a signal-to-noise ratio prior map which provides the spatial-varying
information for low-light image enhancement. Moreover, we leverage unsupervised
learning to construct the optimization objective based on Retinex model, to
guide the training of proposed network. Experimental results demonstrate that
the proposed model is competitive with the baseline models