Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network
This work addresses the problem of real-time video deblurring for computer vision applications, with incremental improvements in efficiency and dataset quality.
The authors tackled real-world video deblurring by proposing an efficient recurrent neural network (ESTRNN) and introducing a new benchmark dataset (BSD) collected with a co-axis beam splitter system, achieving better deblurring performance with less computational cost compared to state-of-the-art methods.
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.