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Proceedings Paper

Unified optimization framework for L2, L1, and/or L0 constrained image reconstruction
Author(s): Masayuki Tanaka; Masatoshi Okutomi
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Paper Abstract

In this paper, we propose a unified optimization framework for L2, L1, and/or L0 constrained image reconstruction. First, we generalize cost functions for image reconstruction, which consist of a fidelity term with L2 norm and constraint terms with L2, L1, and/or L0 norms. This generalized cost function covers many types of existing cost functions for image reconstruction. Then, we show that this generalized cost function can be optimized by the alternating direction method of multipliers (ADMM). The ADMM is a well-known iterative optimization approach for convex problems. Experimental results demonstrate that the proposed unified optimization framework is applicable to a wide range of applications.

Paper Details

Date Published: 1 May 2017
PDF: 10 pages
Proc. SPIE 10222, Computational Imaging II, 102220J (1 May 2017); doi: 10.1117/12.2257957
Show Author Affiliations
Masayuki Tanaka, Tokyo Institute of Technology (Japan)
Masatoshi Okutomi, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 10222:
Computational Imaging II
Abhijit Mahalanobis; Amit Ashok; Lei Tian; Jonathan C. Petruccelli; Kenneth S. Kubala, Editor(s)

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