DEFormer: DCT-driven Enhancement Transformer for Low-light Image and
  Dark Vision
Xiangchen Yin Zhenda Yu Xin Gao Ran Ju Xiao Sun Xinyu Zhang
The goal of low-light image enhancement is to restore the color and details
of the image and is of great significance for high-level visual tasks in
autonomous driving. However, it is difficult to restore the lost details in the
dark area by relying only on the RGB domain. In this paper we introduce
frequency as a new clue into the network and propose a novel DCT-driven
enhancement transformer (DEFormer). First, we propose a learnable frequency
branch (LFB) for frequency enhancement contains DCT processing and
curvature-based frequency enhancement (CFE). CFE calculates the curvature of
each channel to represent the detail richness of different frequency bands,
then we divides the frequency features, which focuses on frequency bands with
richer textures. In addition, we propose a cross domain fusion (CDF) for
reducing the differences between the RGB domain and the frequency domain. We
also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively
improves the performance of the detector, bringing 2.1% and 3.4% improvement in
ExDark and DARK FACE datasets on mAP respectively.