Motion Deblurring for Plenoptic Images
Paramanand Chandramouli, Paolo Favaro, Daniele Perrone

TL;DR
This paper introduces a novel method for blind motion deblurring of light field images captured by plenoptic cameras, addressing the unique challenges posed by the camera's sampling and defocus characteristics.
Contribution
It proposes a new, efficient model for light field motion blur and adapts a regularized blind deconvolution approach to handle the complex imaging model of plenoptic cameras.
Findings
Effective deblurring on synthetic light field data
Successful application to real camera data
Handles practical issues like distortion correction
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 uniform (shift-invariant) blur of Lambertian objects, i.e., when objects are sufficiently far away from the camera to be approximately invariant to depth changes and their reflectance does not vary with the viewing direction. We introduce a highly parallelizable model for light field motion blur…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
