3D-object space reconstruction from planar recorded data of 3D-integral images

Silvia Manolache Cirstea, S. Y. Kung, Malcolm McCormick, Amar Aggoun

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)5-18
Number of pages14
JournalJournal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
Issue number1
StatePublished - Aug 2003

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Electrical and Electronic Engineering


  • Durbin-Willshaw scheme
  • Gradient descent
  • Integral imaging
  • Inverse problems
  • Object space reconstruction
  • Regularization methods


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