Local Bidirection Recurrent Network for Efficient Video Deblurring with the Fused Temporal Merge Module

Chen Li, Li Song, Rong Xie, Wenjun Zhang

Abstract: Video deblurring methods exploit the correlation between consecutive blurry inputs to generate sharp frames. However, designing an effective and efficient method is a challenging problem for video deblurring. To guarantee the effectiveness and further improve the deblurring performance, we adopt the recurrent-based method as the baseline and reconsider the recurrent mechanism as well as the temporal feature alignment in the state-of-the-art methods. For the recurrent mechanism, we add the local backward connection to the global forward recurrent backbone to effectively exploit accurate future information. For the temporal alignment, we adopt a fused temporal merge module that exploits the superiority of flow-based and kernel-based methods with progressive correlation volumes estimation. In addition, we evaluate our method with both synthetic datasets (GoPro, DVD) and a realistic dataset (BSD). The experimental results demonstrate that our method achieves significant performance improvement with a slight computational cost increase against the state-of-the-art video deblurring methods. The extended ablation studies verify the effectiveness of our model.