Low-light image enhancement restores the colors and details of a single image and improves high-level visual tasks. However, restoring the lost details in the dark area is still a challenge relying only on the RGB domain. In this paper, we delve into frequency as a new clue into the model and propose a DCT-driven enhancement transformer (DEFormer) framework. First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE) to represent frequency features. Additionally, we propose a cross domain fusion (CDF) to reduce the differences between the RGB domain and the frequency domain. Our DEFormer has achieved superior results on the LOL and MIT-Adobe FiveK datasets, improving the dark detection performance.
@article{arxiv.2309.06941,
title = {DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision},
author = {Xiangchen Yin and Zhenda Yu and Xin Gao and Xiao Sun},
journal= {arXiv preprint arXiv:2309.06941},
year = {2025}
}