Richardson-Lucy Deblurring for Moving Light Field Cameras
This addresses deblurring for moving light field cameras, which is incremental as it extends existing 2-D methods to 4-D light fields.
The paper tackles the problem of deblurring 4-D light fields from camera motion in complex 3-D scenes without depth estimation, by generalizing Richardson-Lucy deblurring with a novel regularization term, and demonstrates effectiveness on rendered and captured scenes with quantitative performance established using an industrial robot arm.
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.