Optical Flow Estimation from a Single Motion-blurred Image

Authors

  • Dawit Mureja Argaw KAIST Robotics and Computer Vision Lab., Daejeon, Korea
  • Junsik Kim KAIST Robotics and Computer Vision Lab., Daejeon, Korea
  • Francois Rameau KAIST Robotics and Computer Vision Lab., Daejeon, Korea
  • Jae Won Cho KAIST Robotics and Computer Vision Lab., Daejeon, Korea
  • In So Kweon KAIST Robotics and Computer Vision Lab., Daejeon, Korea

Keywords:

Motion & Tracking

Abstract

In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems. In this work, we propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner. We design our network with transformer networks to learn globally and locally varying motions from encoded features of a motion-blurred input, and decode left and right frame features without explicit frame supervision. A flow estimator network is then used to estimate optical flow from the decoded features in a coarse-to-fine manner. We qualitatively and quantitatively evaluate our model through a large set of experiments on synthetic and real motion-blur datasets. We also provide in-depth analysis of our model in connection with related approaches to highlight the effectiveness and favorability of our approach. Furthermore, we showcase the applicability of the flow estimated by our method on deblurring and moving object segmentation tasks.

Downloads

Published

2021-05-18

How to Cite

Argaw, D. M., Kim, J., Rameau, F., Cho, J. W., & Kweon, I. S. (2021). Optical Flow Estimation from a Single Motion-blurred Image. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 891-900. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16172

Issue

Section

AAAI Technical Track on Computer Vision I