Solving non-negative linear inverse problems with the NeAREst method

Xiaobai Sun; Nikos P. Pitsianis

doi:10.1117/12.795479

3 September 2008 Solving non-negative linear inverse problems with the NeAREst method

Xiaobai Sun, Nikos P. Pitsianis

Proceedings Volume 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII; 707402 (2008) https://doi.org/10.1117/12.795479
Event: Optical Engineering + Applications, 2008, San Diego, California, United States

Abstract

This paper introduces the theoretical development of a numerical method, named NeAREst, for solving non-negative linear inverse problems, which arise often from physical or probabilistic models, especially, in image estimation with limited and indirect measurements. The Richardson-Lucy (RL) iteration is omnipresent in conventional methods that are based on probabilistic assumptions, arguments and techniques. Without resorting to probabilistic assumptions, NeAREst retains many appealing properties of the RL iteration by utilizing it as the substrate process and provides much needed mechanisms for acceleration as well as for selection of a target solution when many admissible ones exist.

Citation Download Citation

Xiaobai Sun and Nikos P. Pitsianis "Solving non-negative linear inverse problems with the NeAREst method", Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 707402 (3 September 2008); https://doi.org/10.1117/12.795479

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