Graph-based regularization for spherical signal interpolation

This paper addresses the problem of the interpolation of 2-d spherical signals from non-uniformly sampled and noisy data. We propose a graph-based regularization algorithm to improve the signal reconstructed by local interpolation methods such as nearest neighbour or kernel-based interpolation algorithms. We represent the signal as a function on a graph where weights are adapted to the particular geometry of the sphere. We then solve a total variation (TV) minimization problem with a modified version of Chambolle's algorithm. Experimental results with noisy and uncomplete datasets show that the regularization algorithm is able to improve the result of local interpolation schemes in terms of reconstruction quality.


Published in:
Proceedings of ICASSP
Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, Texas, USA, March 14-19, 2010
Year:
2010
Keywords:
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 Record created 2009-12-14, last modified 2018-03-17

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