Understanding Camera Trade-Offs through a Bayesian Analysis of Light Field Projections

  • Anat Levin
  • William T. Freeman
  • Frédo Durand
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)


Computer vision has traditionally focused on extracting structure, such as depth, from images acquired using thin-lens or pinhole optics. The development of computational imaging is broadening this scope; a variety of unconventional cameras do not directly capture a traditional image anymore, but instead require the joint reconstruction of structure and image information. For example, recent coded aperture designs have been optimized to facilitate the joint reconstruction of depth and intensity. The breadth of imaging designs requires new tools to understand the tradeoffs implied by different strategies.

This paper introduces a unified framework for analyzing computational imaging approaches. Each sensor element is modeled as an inner product over the 4D light field. The imaging task is then posed as Bayesian inference: given the observed noisy light field projections and a prior on light field signals, estimate the original light field. Under common imaging conditions, we compare the performance of various camera designs using 2D light field simulations. This framework allows us to better understand the tradeoffs of each camera type and analyze their limitations.


Sensor Element Stereo Camera Depth Discontinuity Band Limited Signal Joint Reconstruction 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Anat Levin
    • 1
  • William T. Freeman
    • 1
    • 2
  • Frédo Durand
    • 1
  1. 1.MIT CSAIL 
  2. 2.Adobe Systems 

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