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

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.