Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

Desmal, Abdulla; Bagci, Hakan

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

Files

poster_IN_Desmal.pdf (930.48 KB)

Type

Poster

Authors

Desmal, Abdulla

Bagci, Hakan

KAUST Department

Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program

Date

2014-05-04

Abstract

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.