  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.
