Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37996
Title: Hierarchical adaptive regularisation method for depth extraction from planar recording of 3D-integral images
Authors: Manolache, S
Mccormick, M
Kung, SY
Keywords: Adaptive signal processing
Feature extraction
Image reconstruction
Image resolution
Inverse problems
Least squares approximations
Optical transfer function
Issue Date: 2001
Source: ICASSP 2001 : International Conference on Acoustics, Speech, and Signal Processing ; May 7 - 11, 2001, Salt Lake City, Utah, p. 1433-1436 (CD) How to cite?
Abstract: The paper presents a novel algorithm for object space reconstruction from the planar (2D) recorded data set of a 3D-integral image. The integral imaging system is described and the associated point spread function is given. The space data extraction is formulated as an inverse problem, which proves ill-conditioned, and tackled by using a hierarchical multiresolution strategy and imposing additional conditions to the sought solution. The hierarchical strategy and the two-phase adaptive constrained 3D-reconstruction algorithm based on the use of two sigmoid functions are presented. Finally, illustrative simulation results are given
URI: http://hdl.handle.net/10397/37996
ISBN: 0-7803-7041-4
DOI: 10.1109/ICASSP.2001.941199
Appears in Collections:Conference Paper

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