  It is highly desired but challenging to acquire high-quality photos with
clear content in low-light environments. Although multi-image processing
methods (using burst, dual-exposure, or multi-exposure images) have made
significant progress in addressing this issue, they typically focus on specific
restoration or enhancement problems, and do not fully explore the potential of
utilizing multiple images. Motivated by the fact that multi-exposure images are
complementary in denoising, deblurring, high dynamic range imaging, and
super-resolution, we propose to utilize exposure bracketing photography to
unify image restoration and enhancement tasks in this work. Due to the
difficulty in collecting real-world pairs, we suggest a solution that first
pre-trains the model with synthetic paired data and then adapts it to
real-world unlabeled images. In particular, a temporally modulated recurrent
network (TMRNet) and self-supervised adaptation method are proposed. Moreover,
we construct a data simulation pipeline to synthesize pairs and collect
real-world images from 200 nighttime scenarios. Experiments on both datasets
show that our method performs favorably against the state-of-the-art
multi-image processing ones. The dataset, code, and pre-trained models are
available at https://github.com/cszhilu1998/BracketIRE.
