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.

翻译：低光条件下产生的失真不仅视觉上令人不适，还会降低计算机视觉任务的性能。事实证明，恢复与增强技术具有显著效益。然而，目前仅有少数增强方法明确针对低光条件下采集的视频而设计。本文提出一种时空对齐SUNet（STA-SUNet）模型，该模型以Swin Transformer为骨干网络，用于捕获低光视频特征并利用其时空相关性。STA-SUNet模型在一个新颖的完全配准数据集（BVI）上训练，该数据集包含在不同光照条件下拍摄的动态场景。通过在三个测试数据集上与多种其他模型进行对比分析，该模型在所有数据集上均表现出卓越的自适应能力，获得了最高的PSNR和SSIM值。该模型在极端低光条件下尤其有效，能产生相当良好的可视化结果。