Photosequencing of Motion Blur using Short and Long Exposures
This work addresses the challenge of recovering temporal and spatial details from motion blur in computational photography, offering an incremental improvement over existing methods.
The paper tackled the problem of converting motion-blurred images into sequences of sharp images by capturing two short exposures alongside a long exposure and using a recursive blur decomposition strategy. The method resolved fast and fine motions better than prior works, as validated with machine vision cameras.
Photosequencing aims to transform a motion blurred image to a sequence of sharp images. This problem is challenging due to the inherent ambiguities in temporal ordering as well as the recovery of lost spatial textures due to blur. Adopting a computational photography approach, we propose to capture two short exposure images, along with the original blurred long exposure image to aid in the aforementioned challenges. Post-capture, we recover the sharp photosequence using a novel blur decomposition strategy that recursively splits the long exposure image into smaller exposure intervals. We validate the approach by capturing a variety of scenes with interesting motions using machine vision cameras programmed to capture short and long exposure sequences. Our experimental results show that the proposed method resolves both fast and fine motions better than prior works.