Motion-blurred Video Interpolation and Extrapolation

TL;DR

This paper introduces an end-to-end framework that jointly deblurs, interpolates, and extrapolates sharp video frames from motion-blurred videos by estimating pixel-level motion and ensuring temporal coherence.

Contribution

It presents a novel unified approach combining motion estimation and frame prediction to handle both deblurring and temporal upsampling in videos.

Findings

Outperforms existing methods on high-speed video datasets.

Effectively recovers sharp frames from severely blurred videos.

Ensures temporal consistency across predicted frames.

Abstract

Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and temporal upsampling. A broad range of research attempted to recover clean frames from blurred image sequences or temporally upsample frames by interpolation, yet there are very limited studies handling both problems jointly. In this work, we present a novel framework for deblurring, interpolating and extrapolating sharp frames from a motion-blurred video in an end-to-end manner. We design our framework by first learning the pixel-level motion that caused the blur from the given inputs via optical flow estimation and then predict multiple clean frames by warping the decoded features with the estimated flows. To ensure temporal coherence across predicted frames and address potential temporal ambiguity, we propose a simple, yet effective flow-based rule. The effectiveness and favorability of our approach are highlighted through extensive qualitative and quantitative evaluations on motion-blurred datasets from high speed videos.