论文标题
关于BasicVSR ++对视频脱张和变形的概括
On the Generalization of BasicVSR++ to Video Deblurring and Denoising
论文作者
论文摘要
长期信息的开发一直是视频恢复的长期问题。最近的BasicVSR和BASICVSR ++通过长期传播和有效的对齐方式在视频超分辨率中表现出色。他们的成功导致了一个问题，即是否可以将它们转移到不同的视频恢复任务中。在这项工作中，我们将BASICVSR ++扩展到用于视频恢复任务的通用框架。在输入和输出具有相同空间尺寸的任务中，输入分辨率通过稳定的卷积以维持效率而降低。只有从BASICVSR ++发生的最小变化，所提出的框架在各种视频恢复任务中都具有出色的效率，包括视频Deblurring和Denoising。值得注意的是，BASICVSR ++的性能与基于变压器的方法相当，最多79％的参数降低和44倍加速。有希望的结果表明，不仅仅是视频超级分辨率，在视频恢复任务中传播和一致性的重要性。代码和模型可在https://github.com/ckkelvinchan/basicvsr_plusplus上找到。
The exploitation of long-term information has been a long-standing problem in video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in video super-resolution through long-term propagation and effective alignment. Their success has led to a question of whether they can be transferred to different video restoration tasks. In this work, we extend BasicVSR++ to a generic framework for video restoration tasks. In tasks where inputs and outputs possess identical spatial size, the input resolution is reduced by strided convolutions to maintain efficiency. With only minimal changes from BasicVSR++, the proposed framework achieves compelling performance with great efficiency in various video restoration tasks including video deblurring and denoising. Notably, BasicVSR++ achieves comparable performance to Transformer-based approaches with up to 79% of parameter reduction and 44x speedup. The promising results demonstrate the importance of propagation and alignment in video restoration tasks beyond just video super-resolution. Code and models are available at https://github.com/ckkelvinchan/BasicVSR_PlusPlus.
