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.

翻译：低光照图像增强旨在恢复单幅图像的色彩与细节，并提升高层视觉任务的性能。然而，仅依靠RGB域来恢复暗区丢失的细节仍具挑战。本文深入探索将频率信息作为模型的新线索，提出了一种基于DCT的增强Transformer（DEFormer）框架。首先，我们提出一种用于频率增强的可学习频率分支（LFB），其包含DCT处理与基于曲率的频率增强（CFE）模块，以表征频率特征。此外，我们提出跨域融合（CDF）模块来减小RGB域与频率域之间的差异。我们的DEFormer在LOL和MIT-Adobe FiveK数据集上取得了优异的结果，并提升了暗光检测性能。