  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.
