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Proceedings Paper

Blind deconvolution of depth-of-field limited full-field lidar data by determination of focal parameters
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Paper Abstract

We present a new two-stage method for parametric spatially variant blind deconvolution of full-field Amplitude Modulated Continuous Wave lidar image pairs taken at different aperture settings subject to limited depth of field. A Maximum Likelihood based focal parameter determination algorithm uses range information to reblur the image taken with a smaller aperture size to match the large aperture image. This allows estimation of focal parameters without prior calibration of the optical setup and produces blur estimates which have better spatial resolution and less noise than previous depth from defocus (DFD) blur measurement algorithms. We compare blur estimates from the focal parameter determination method to those from Pentland's DFD method, Subbarao's S-Transform method and estimates from range data/the sampled point spread function. In a second stage the estimated focal parameters are applied to deconvolution of total integrated intensity lidar images improving depth of field. We give an example of application to complex domain lidar images and discuss the trade-off between recovered amplitude texture and sharp range estimates.

Paper Details

Date Published: 27 January 2010
PDF: 12 pages
Proc. SPIE 7533, Computational Imaging VIII, 75330B (27 January 2010); doi: 10.1117/12.838553
Show Author Affiliations
John P. Godbaz, Univ. of Waikato (New Zealand)
Michael J. Cree, Univ. of Waikato (New Zealand)
Adrian A. Dorrington, Univ. of Waikato (New Zealand)


Published in SPIE Proceedings Vol. 7533:
Computational Imaging VIII
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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