Online Video Deblurring via Dynamic Temporal Blending Network

TL;DR

This paper introduces a real-time online video deblurring method using a spatio-temporal recurrent network with dynamic temporal blending, effectively removing large motion blurs in dynamic scenes.

Contribution

The paper presents a novel online deblurring network with an extended receptive field and a dynamic temporal blending layer for improved temporal consistency and speed.

Findings

Achieves real-time performance in video deblurring

Removes large motion blur effectively in dynamic scenes

Outperforms existing batch-based methods in accuracy

Abstract

State-of-the-art video deblurring methods are capable of removing non-uniform blur caused by unwanted camera shake and/or object motion in dynamic scenes. However, most existing methods are based on batch processing and thus need access to all recorded frames, rendering them computationally demanding and time consuming and thus limiting their practical use. In contrast, we propose an online (sequential) video deblurring method based on a spatio-temporal recurrent network that allows for real-time performance. In particular, we introduce a novel architecture which extends the receptive field while keeping the overall size of the network small to enable fast execution. In doing so, our network is able to remove even large blur caused by strong camera shake and/or fast moving objects. Furthermore, we propose a novel network layer that enforces temporal consistency between consecutive frames by dynamic temporal blending which compares and adaptively (at test time) shares features obtained at different time steps. We show the superiority of the proposed method in an extensive experimental evaluation.