Nighttime photography encounters escalating challenges in extremely low-light
conditions, primarily attributable to the ultra-low signal-to-noise ratio. For
real-world deployment, a practical solution must not only produce visually
appealing results but also require minimal computation. However, most existing
methods are either focused on improving restoration performance or employ
lightweight models at the cost of quality. This paper proposes a lightweight
network that outperforms existing state-of-the-art (SOTA) methods in low-light
enhancement tasks while minimizing computation. The proposed network
incorporates Siamese Self-Attention Block (SSAB) and Skip-Channel Attention
(SCA) modules, which enhance the model's capacity to aggregate global
information and are well-suited for high-resolution images. Additionally, based
on our analysis of the low-light image restoration process, we propose a
Two-Stage Framework that achieves superior results. Our model can restore a UHD
4K resolution image with minimal computation while keeping SOTA restoration
quality.

本文提出了一种轻量级网络，采用 Siamese Self-Attention Block (SSAB) 和 Skip-Channel Attention (SCA) 模块，提高了模型对全局信息的聚合能力，适用于高分辨率图像，并且基于对低光图像恢复过程的分析，提出了一个优于现有方法的二阶段框架，能够在保持最先进的恢复质量的同时，用最少的计算量恢复一张 UHD 4K 分辨率的图像。