Nonparametric least squares regression for image reconstruction on the sphere

  • Authors:

  • Tamara Tošic;Ivana Tošic;Pascal Frossard

  • Affiliations:

  • Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

  • Venue:
  • PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper addresses the problem of interpolating signals defined on a 2-d sphere from non-uniform samples. We present an interpolation method based on locally weighted linear and nonlinear regression, which takes into account the differences in importance of neighboring samples for signal reconstruction. We show that for optimal kernel function variance, the proposed method performs interpolation more accurately than the nearest neighbor method, especially in noisy conditions. Moreover, this method does not have memory limitations which set the upper bound on the possible interpolation points number, like in the method presented in [1].