用于低光照视频增强的时空对齐SUNet模型
A Spatio-temporal Aligned SUNet Model for Low-light Video Enhancement
AI总结：
针对低光照视频增强方法匮乏的问题，提出以Swin Transformer为主干的时空对齐STA-SUNet模型，利用时空相关性，在BVI数据集训练后于三个测试集上取得最高PSNR和SSIM，极端低光下效果突出。
摘要：
低光照条件导致的失真不仅在视觉上令人不适，还会降低计算机视觉任务的性能。事实证明，恢复与增强工作极具价值。然而，专门针对低光照条件下拍摄的视频设计的增强方法数量十分有限。我们提出一种时空对齐SUNet（STA-SUNet）模型，以Swin Transformer为主干网络，用于提取低光照视频特征并利用其时空相关性。STA-SUNet模型在一个全新的完全配准数据集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。我们进一步在三个测试数据集上将该模型与多种其他模型进行对比分析。该模型在所有数据集上均展现出更优的适应性，取得了最高的PSNR和SSIM值。它在极端低光照条件下效果尤为突出，能生成相当不错的可视化结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
