## Bounding pixels in computational imaging |

Applied Optics, Vol. 52, Issue 10, pp. D55-D63 (2013)

http://dx.doi.org/10.1364/AO.52.000D55

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### Abstract

We consider computational imaging problems where we have an insufficient number of measurements to uniquely reconstruct the object, resulting in an ill-posed inverse problem. Rather than deal with this via the usual regularization approach, which presumes additional information which may be incorrect, we seek bounds on the pixel values of the reconstructed image. Formulating the inverse problem as an optimization problem, we find conditions for which a system’s measurements can produce a bounded result for both the linear case and the non-negative case (e.g., intensity imaging). We also consider the problem of selecting measurements to yield the most bounded results. Finally we simulate examples of the application of bounded estimation to different two-dimensional multiview systems.

© 2013 Optical Society of America

**OCIS Codes**

(110.1758) Imaging systems : Computational imaging

(110.6955) Imaging systems : Tomographic imaging

**History**

Original Manuscript: November 1, 2012

Revised Manuscript: February 25, 2013

Manuscript Accepted: March 7, 2013

Published: March 22, 2013

**Virtual Issues**

Vol. 8, Iss. 5 *Virtual Journal for Biomedical Optics*

**Citation**

Keith Dillon and Yeshaiahu Fainman, "Bounding pixels in computational imaging," Appl. Opt. **52**, D55-D63 (2013)

http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-52-10-D55

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