Richardson-Lucy Deblurring for Moving Light Field Cameras

TL;DR

This paper extends Richardson-Lucy deblurring to 4-D light fields, effectively handling complex 3-D scene motion blur without depth estimation, and introduces a new regularization to preserve parallax while reducing noise.

Contribution

It generalizes RL deblurring to light fields with a novel rendering approach and regularization, enabling motion blur correction in complex 3-D scenes without depth estimation.

Findings

Effective deblurring on rendered and real light field scenes

Quantitative performance validated with known robot trajectories

Convergence to maximum-likelihood estimate proven mathematically

Abstract

We generalize Richardson-Lucy (RL) deblurring to 4-D light fields by replacing the convolution steps with light field rendering of motion blur. The method deals correctly with blur caused by 6-degree-of-freedom camera motion in complex 3-D scenes, without performing depth estimation. We introduce a novel regularization term that maintains parallax information in the light field while reducing noise and ringing. We demonstrate the method operating effectively on rendered scenes and scenes captured using an off-the-shelf light field camera. An industrial robot arm provides repeatable and known trajectories, allowing us to establish quantitative performance in complex 3-D scenes. Qualitative and quantitative results confirm the effectiveness of the method, including commonly occurring cases for which previously published methods fail. We include mathematical proof that the algorithm converges to the maximum-likelihood estimate of the unblurred scene under Poisson noise. We expect extension to blind methods to be possible following the generalization of 2-D Richardson-Lucy to blind deconvolution.