  As the quality of optical sensors improves, there is a need for processing
large-scale images. In particular, the ability of devices to capture ultra-high
definition (UHD) images and video places new demands on the image processing
pipeline. In this paper, we consider the task of low-light image enhancement
(LLIE) and introduce a large-scale database consisting of images at 4K and 8K
resolution. We conduct systematic benchmarking studies and provide a comparison
of current LLIE algorithms. As a second contribution, we introduce LLFormer, a
transformer-based low-light enhancement method. The core components of LLFormer
are the axis-based multi-head self-attention and cross-layer attention fusion
block, which significantly reduces the linear complexity. Extensive experiments
on the new dataset and existing public datasets show that LLFormer outperforms
state-of-the-art methods. We also show that employing existing LLIE methods
trained on our benchmark as a pre-processing step significantly improves the
performance of downstream tasks, e.g., face detection in low-light conditions.
The source code and pre-trained models are available at
https://github.com/TaoWangzj/LLFormer.
