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Richardson-Lucy Deblurring for Moving Light Field Cameras

(left) Motion blur in 3-D scenes takes on a complex variety of shapes; (right) We introduce a light-field generalization of Richardson-Lucy deblurring which deals correctly with complex 3-D geometry and 6-DOF camera motion. No depth estimation is performed, only the camera’s trajectory is required.

We generalize Richardson-Lucy deblurring to 4-D light fields by replacing the convolution steps with light field rendering of motion blur. We include a novel regularization term that maintains parallax information in the light field, and employ 4-D anisotropic total variation to reduce noise and ringing.

  • Generalization of Richardson-Lucy deblurring to moving light field cameras
  • 6-DOF camera motion in arbitrary 3D scenes
  • Deblurring of nonuniform apparent motion without depth estimation
  • Novel parallax-preserving light field regularization
  • Low-dimensional motion model allows efficient convergence
  • Mathematical proof that the algorithm converges to the ML estimate under Poisson noise

Limitations: Like conventional Richardson-Lucy, this method is not blind. However, we anticipate that it can be extended to blind deblurring by mirroring developments in 2D deconvolution.

Publications

•  D. G. Dansereau, A. Eriksson, and J. Leitner, “Richardson-lucy deblurring for moving light field cameras,” CVPR workshop on Light Fields for Computer Vision (CVPR:LF4CV), in press, 2017. Available here.

Collaborators

I started this work with Juxi Leitner and Anders Eriksson while at the Australian Centre for Robotic Vision at the Queensland University of Technology, and have since contributed from my position at Stanford.

Presentations

Presentation from the 2017 CVPR Light Fields for Computer Vision (LF4CV) workshop.

Acknowledgments

This research was supported by the Australian Research Council through the Centre of Excellence for Robotic Vision (project number CE140100016) and grant DE130101775. Computational resources and services provided by the HPC and Research Support Group, Queensland University of Technology, Brisbane, Australia.

Themes