Recursive Neural Network for Video Deblurring

Video deblurring is still a challenging low-level vision task since spatio-temporal characteristics across both the spatial and temporal domains are difficult to model. In this article, to model the temporal information, we develop a non-local block which estimates inter-frame similarity and inter-frame difference. Specially, for modeling the spatial characteristics and restoring sharp frame details, we propose a recursive block that iteratively refines feature maps generated at the last iteration. In addition, a novel temporal loss function is introduced to ensure the temporal consistency of generated frames. Experimental results on public datasets demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively.

Automated summary

Video deblurring is a challenging task that requires accurate modeling of spatial and temporal characteristics. This article proposes a method to simulate temporal information and restore frame details, introducing a new loss function to ensure temporal consistency in generated frames.