Image Reconstruction by Prioritized Incremental Normalized Convolution

  • Anders Landström
  • Frida Nellros
  • Håkan Jonsson
  • Matthew Thurley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution.

The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.

Keywords

image reconstruction hole filling normalized convolution 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anders Landström
    • 1
  • Frida Nellros
    • 1
  • Håkan Jonsson
    • 1
  • Matthew Thurley
    • 1
  1. 1.Department of Computer Science, Electrical and Space EngineeringLuleå University of TechnologyLuleåSweden

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