3D-Object Space Reconstruction from Planar Recorded Data of 3D-Integral Images

  • Authors:

  • Silvia Manolache Cirstea;S. Y. Kung;Malcolm McCormick;Amar Aggoun

  • Affiliations:

  • Department of Electrical Engineering, Princeton University, Princeton, NJ 08540, USA&semi/ Department of Engineering and Technology, De Montfort University, The Gateway, Leicester LE1 9BH, UK;Department of Electrical Engineering, Princeton University, Princeton, NJ 08540, USA;Department of Engineering and Technology, De Montfort University, The Gateway, Leicester LE1 9BH, UK;Department of Engineering and Technology, De Montfort University, The Gateway, Leicester LE1 9BH, UK

  • Venue:
  • Journal of VLSI Signal Processing Systems
  • Year:
  • 2003

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Abstract

The paper presents a novel algorithm for object space reconstruction from the planar (2D) recorded data set of a 3D-integral image. The integral imaging system is described and the associated point spread function is given. The space data extraction is formulated as an inverse problem, which proves ill-conditioned, and tackled by imposing additional conditions to the sought solution. An adaptive constrained 3D-reconstruction regularization algorithm based on the use of a sigmoid function is presented. A hierarchical multiresolution strategy which employes the adaptive constrained algorithm to obtain highly accurate intensity maps of the object space is described. The depth map of the object space is extracted from the intensity map using a weighted Durbin–Willshaw algorithm. Finally, illustrative simulation results are given.