Joint Blind Motion Deblurring and Depth Estimation of Light Field

Dongwoo Lee, Haesol Park, In Kyu Park, Kyoung Mu Lee; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 288-303

Abstract


Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods.

Related Material


[pdf]
[bibtex]
@InProceedings{Lee_2018_ECCV,
author = {Lee, Dongwoo and Park, Haesol and Park, In Kyu and Lee, Kyoung Mu},
title = {Joint Blind Motion Deblurring and Depth Estimation of Light Field},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}