  In real-world scenarios, due to a series of image degradations, obtaining
high-quality, clear content photos is challenging. While significant progress
has been made in synthesizing high-quality images, previous methods for image
restoration and enhancement often overlooked the characteristics of different
degradations. They applied the same structure to address various types of
degradation, resulting in less-than-ideal restoration outcomes. Inspired by the
notion that high/low frequency information is applicable to different
degradations, we introduce HLNet, a Bracketing Image Restoration and
Enhancement method based on high-low frequency decomposition. Specifically, we
employ two modules for feature extraction: shared weight modules and non-shared
weight modules. In the shared weight modules, we use SCConv to extract common
features from different degradations. In the non-shared weight modules, we
introduce the High-Low Frequency Decomposition Block (HLFDB), which employs
different methods to handle high-low frequency information, enabling the model
to address different degradations more effectively. Compared to other networks,
our method takes into account the characteristics of different degradations,
thus achieving higher-quality image restoration.
