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Lens-Based Depth Estimation for Multi-focus Plenoptic Cameras

  • Oliver Fleischmann
  • Reinhard Koch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

Multi-focus portable plenoptic camera devices provide a reasonable tradeoff between spatial and angular resolution while enlarging the depth of field of a standard camera. Many applications using the data captured by these camera devices require or benefit from correspondences established between the single microlens images. In this work we propose a lens-based depth estimation scheme based on a novel adaptive lens selection strategy. Coarse depth estimates serve as indicators for suitable target lenses. The selection criterion accounts for lens overlap and the amount of defocus blur between the reference and possible target lenses. The depth maps are regularized using a semi-global strategy. For insufficiently textured scenes, we further incorporate a semi-global coarse regularization with respect to the lens-grid. In contrast to algorithms operating on the complete lightfield, our algorithm has a low memory footprint. The resulting per-lens dense depth maps are well suited for volumetric surface reconstruction techniques. We show that our selection strategy achieves similar error rates as selection strategies with a fixed number of lenses, while being computationally less time consuming. Results are presented for synthetic as well as real-world datasets.

Keywords

Depth Estimation Microlens Array Disparity Estimate Texture Scene Standard Camera 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of KielKielGermany

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