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

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.
Conference Contribution
Type of thesis
John P. Godbaz, Michael J. Cree, and Adrian A. Dorrington, "Blind deconvolution of depth-of-field limited full-field lidar data by determination of focal parameters," Computational Imaging VIII, Charles A. Bouman, Ilya Pollak, Patrick J. Wolfe, Editors, Proc. SPIE, 7533, 75330B (2010).
Society of Photo-Optical Instrumentation Engineers
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