1 April 1990 Three-dimensional image restoration using constrained optimization techniques
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Optical Engineering, 29(4), (1990). doi:10.1117/12.55607
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.
K. Venkatesh Prasad, Richard J. Mammone, Jay Yogeshwar, "Three-dimensional image restoration using constrained optimization techniques," Optical Engineering 29(4), (1 April 1990). http://dx.doi.org/10.1117/12.55607
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KEYWORDS
Image segmentation

Point spread functions

Image restoration

Cameras

Image processing

3D image processing

Image processing algorithms and systems

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