Paper Info
Title
Single Frequency Inverse Obstacle Scattering: A Sparsity Constrained Linear Sampling Method Approach.
Abstract
The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.
Year
DOI
Venue
2012
10.1109/TIP.2011.2177992
IEEE Transactions on Image Processing
Keywords
Field
DocType
scattering,algorithms,inverse problem,apertures,inverse scattering,inverse problems,sparse matrices,mathematical model,total variation,linear models,sample size,tikhonov regularization,image reconstruction,sampling methods
Tikhonov regularization,Iterative reconstruction,Computer vision,Obstacle,Regularization (mathematics),Inverse problem,Artificial intelligence,Sampling (statistics),Mathematics,Inverse scattering problem,Sparse matrix
Journal
Volume
Issue
ISSN
21
4
1057-7149
Citations 
PageRank 
References 
2
0.40
6
Authors
4
Name
Order
Citations
PageRank
Hatim F. Alqadah1241.44
Matthew Ferrara2243.35
Howard Fan3627.97
Jason T. Parker41928.11