CVG Projects

Plenoptic Image Motion Deblurring

Paramanand Chandramouli, Meiguang Jin, Daniele Perrone, and Paolo Favaro

Universit├Ąt Bern, Bern, Switzerland

Abstract

We address for the first time the issue of motion blur in light field images captured from plenoptic cameras. We propose a solution to the estimation of a sharp high resolution scene radiance given a blurry light field image, when the motion blur point spread function is unknown, i.e., the so-called blind deconvolution problem. In a plenoptic camera, the spatial sampling in each view is not only decimated but also defocused. Consequently, current blind deconvolution approaches for traditional cameras are not applicable. Due to the complexity of the imaging model, we investigate first the case of Lambertian objects. We introduce a highly parallelizable scheme to model light field image formation. Our model enables fast GPU implementation of scene texture estimation from light fields. We then adapt a regularized blind deconvolution approach to deblur light fields. The proposed algorithm can handle non-uniform motion blur due to camera shake as demonstrated on both synthetic and real light field data.

Paper

Code

Synthetic example with conv-based implementation.

Real example with matrix product-based implementation (needs GPU and CUDA).

Bibtex

@article{Plenoptic_Deblurring,
  author = {Paramanand Chandramouli and Meiguang Jin and Daniele Perrone and Paolo Favaro},
  title = {Motion Deblurring for Plenoptic Images},
  journal = {arXiv:1408.3686},
  year = {2014}
}