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Optical Engineering

Three-dimensional image restoration using constrained optimization techniques
Author(s): K. Venkatesh Prasad; Richard J. Mammone; Jay Yogeshwar
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Paper Abstract

The restoration of the depth map of a three-dimensional object is formulated as an image-restoration problem. The object is modeled as an opaque discrete visible surface (DVS) in 3-D space. The projection of the radiance and depth information of the DVS onto a two-dimensional image yields an underdetermined system of equations. The 3-D imagerestoration problem seeks to recover the depth information of the DVS from the 2-D image. To uniquely specify a solution, constraints on the estimates of the DVS must be introduced. In this paper an in-focus image is used to provide radiance information. Further, the size of the problem is significantly reduced by limiting the range of possible depths to lie within a fixed interval of the depth values given by an independent coarse depth recovery method. It is shown that additional constraints on both the radiance and the geometry can easily be accommodated by the methods described. The use of three different methods of constrained optimization are investigated for solving the problem. A method based on simulated annealing is shown to offer the best performance. Results of applying the algorithms to test objects using both a simulated and a laboratory optical system are presented.

Paper Details

Date Published: 1 April 1990
PDF: 10 pages
Opt. Eng. 29(4) doi: 10.1117/12.55607
Published in: Optical Engineering Volume 29, Issue 4
Show Author Affiliations
K. Venkatesh Prasad, Rutgers Univ. (United States)
Richard J. Mammone, Rutgers Univ. (United States)
Jay Yogeshwar, Rutgers Univ. (United States)


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