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Shape and Radiance Estimation from the Information Divergence of Blurred Images

  • Paolo Favaro
  • Stefano Soatto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1842)

Abstract

We formulate the problem of reconstructing the shape and radiance of a scene as the minimization of the information divergence between blurred images, and propose an algorithm that is provably convergent and guarantees that the solution is admissible, in the sense of corresponding to a positive radiance and imaging kernel. The motivation for the use of information divergence comes from the work of Csiszár [5], while the fundamental elements of the proof of convergence come from work by Snyder et al. [14], extended to handle unknown imaging kernels (i.e. the shape of the scene).

Keywords

Information Divergence Prior Assumption Blind Deconvolution Radiance Estimation Lens Diameter 
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 2000

Authors and Affiliations

  • Paolo Favaro
    • 1
  • Stefano Soatto
    • 1
  1. 1.Department of Electrical Engineering Electronic Signals and Systems Research LabWashington UniversitySt.LouisUSA

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