Real-Time Video Deblurring via Lightweight Motion Compensation

TL;DR

This paper introduces a lightweight, multi-task framework for real-time video deblurring that combines motion compensation and deblurring in a single efficient network, achieving high quality and speed.

Contribution

It presents a novel multi-task unit that integrates motion compensation and deblurring, enabling real-time processing with flexible quality-speed trade-offs.

Findings

Achieves state-of-the-art deblurring quality.

Runs at 30 fps on DVD dataset.

Reduces computational overhead compared to separate tasks.

Abstract

While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real-time video deblurring framework consisting of a lightweight multi-task unit that supports both video deblurring and motion compensation in an efficient way. The multi-task unit is specifically designed to handle large portions of the two tasks using a single shared network, and consists of a multi-task detail network and simple networks for deblurring and motion compensation. The multi-task unit minimizes the cost of incorporating motion compensation into video deblurring and enables real-time deblurring. Moreover, by stacking multiple multi-task units, our framework provides flexible control between the cost and deblurring quality. We experimentally validate the state-of-the-art deblurring quality of our approach, which runs at a much faster speed compared to previous methods, and show practical real-time performance (30.99dB@30fps measured in the DVD dataset).