Distortions caused by low-light conditions are not only visually unpleasant
but also degrade the performance of computer vision tasks. The restoration and
enhancement have proven to be highly beneficial. However, there are only a
limited number of enhancement methods explicitly designed for videos acquired
in low-light conditions. We propose a Spatio-Temporal Aligned SUNet (STA-SUNet)
model using a Swin Transformer as a backbone to capture low light video
features and exploit their spatio-temporal correlations. The STA-SUNet model is
trained on a novel, fully registered dataset (BVI), which comprises dynamic
scenes captured under varying light conditions. It is further analysed
comparatively against various other models over three test datasets. The model
demonstrates superior adaptivity across all datasets, obtaining the highest
PSNR and SSIM values. It is particularly effective in extreme low-light
conditions, yielding fairly good visualisation results.

我们提出了一种 Swin Transformer 为骨干结构的时空对齐 SUNet（STA-SUNet）模型，用于捕捉低光视频特征并利用它们的时空相关性，通过在新颖统一的 BVI 数据集上进行比较分析，该模型在各个测试数据集上体现出卓越的适应性，获得最高的 PSNR 和 SSIM 值，尤其在极低光条件下效果显著。