Joint Blind Motion Deblurring and Depth Estimation of Light Field

TL;DR

This paper introduces a joint algorithm for deblurring and depth estimation from light fields, effectively handling complex camera motions and scene depths to produce high-quality results.

Contribution

It presents a novel joint estimation method that simultaneously recovers latent images, camera motion, and scene depth from blurred light fields, leveraging multi-view cues.

Findings

Outperforms state-of-the-art light field deblurring methods.

Effectively handles arbitrary 6-DOF camera motion.

Achieves accurate depth estimation alongside deblurring.

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.