Joint Blind Motion Deblurring and Depth Estimation of Light Field
This addresses a challenging ill-posed inverse problem in computational photography for applications like robotics or imaging, but it is incremental as it builds on existing light field and deblurring techniques.
The paper tackles the joint problem of blind motion deblurring and depth estimation from a single blurred 4D light field, achieving high-quality results under arbitrary camera motion and unconstrained scene depth, with experiments showing it outperforms state-of-the-art methods.
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.