Real-World Video Deblurring: A Benchmark Dataset and an Efficient Recurrent Neural Network
Real-World Video Deblurring: A Benchmark Dataset and an Efficient Recurrent Neural Network
复制标题
DOI：
10.1007/s11263-022-01705-6
复制
发表时间：
2021-06
影响因子：
19.5
通讯作者：
Zhihang Zhong;Ye Gao;Yinqiang Zheng;Bo Zheng;Imari Sato
中科院分区：
文献类型：
--
作者：
Zhihang Zhong;Ye Gao;Yinqiang Zheng;Bo Zheng;Imari Sato
由于空间和时间变化模糊本身的复杂性以及低计算成本的要求，真实世界的视频实时去模糊仍然是一项具有挑战性的任务。为了提高网络效率，我们将残差密集块引入RNN单元，从而有效地提取当前帧的空间特征。此外，提出了全局时空注意力模块来融合过去和未来帧的有效分层特征，以帮助更好地对当前帧进行去模糊。另一个急需解决的问题是缺乏真实世界的基准数据集。因此，我们通过使用同轴分束器采集系统收集配对的模糊/清晰视频剪辑，向社区贡献了一个新颖的数据集（BSD）。实验结果表明，与最先进的视频去模糊方法相比，所提出的方法（ESTRNN）可以以更少的计算成本在定量和定性上实现更好的去模糊性能。此外，数据集之间的交叉验证实验说明了 BSD 在合成数据集上的高度通用性。代码和数据集发布于https://github.com/zzh-tech/ESTRNN。
Real-world video deblurring in real time still remains a challenging task due to the complexity of spatially and temporally varying blur itself and the requirement of low computational cost. To improve the network efficiency, we adopt residual dense blocks into RNN cells, so as to efficiently extract the spatial features of the current frame. Furthermore, a global spatio-temporal attention module is proposed to fuse the effective hierarchical features from past and future frames to help better deblur the current frame. Another issue that needs to be addressed urgently is the lack of a real-world benchmark dataset. Thus, we contribute a novel dataset (BSD) to the community, by collecting paired blurry/sharp video clips using a co-axis beam splitter acquisition system. Experimental results show that the proposed method (ESTRNN) can achieve better deblurring performance both quantitatively and qualitatively with less computational cost against state-of-the-art video deblurring methods. In addition, cross-validation experiments between datasets illustrate the high generality of BSD over the synthetic datasets. The code and dataset are released at https://github.com/zzh-tech/ESTRNN.