Analyzing Depth from Coded Aperture Sets

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)


Computational depth estimation is a central task in computer vision and graphics. A large variety of strategies have been introduced in the past relying on viewpoint variations, defocus changes and general aperture codes. However, the tradeoffs between such designs are not well understood. Depth estimation from computational camera measurements is a highly non-linear process and therefore most research attempts to evaluate depth estimation strategies rely on numerical simulations. Previous attempts to design computational cameras with good depth discrimination optimized highly non-linear and non-convex scores, and hence it is not clear if the constructed designs are optimal. In this paper we address the problem of depth discrimination from J images captured using J arbitrary codes placed within one fixed lens aperture. We analyze the desired properties of discriminative codes under a geometric optics model and propose an upper bound on the best possible discrimination. We show that under a multiplicative noise model, the half ring codes discovered by Zhou et al. [1] are near-optimal. When a large number of images are allowed, a multi-aperture camera [2] dividing the aperture into multiple annular rings provides near-optimal discrimination. In contrast, the plenoptic camera of [5] which divides the aperture into compact support circles can achieve at most 50% of the optimal discrimination bound.


Multiplicative Noise Discrimination Score Derivative Power Optical Transfer Function Lens Aperture 
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 2010

Authors and Affiliations

  1. 1.Dep. of Computer Science and Applied MathThe Weizmann Institute of Science 

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