Sparsity-enforced microwave inverse scattering using soft shrinkage thresholding

Zaimaga, Hidayet; Lambert, Marc

doi:10.1109/eusipco.2016.7760268

Cited by 2 publications

(5 citation statements)

References 17 publications

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“…In this section we consider two examples in order to present our proposed method and compare it with the method based on Soft Shrinkage algorithm proposed in [13]. The frequency of the emitting wave is f = 300 MHz and the measured field samples are generated by adding 10 dB white Gaussian noise.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Instead of directly solving the nonlinear inverse problem given by (7) as we did in [13], we adopt a two-step method which firstly consists of finding the equivalent current J i for i = 1, . .…”

Section: Two-step Inversion Proceduresmentioning

confidence: 99%

“…Section III deals with our proposed imaging method based on the sparsity minimization. Section IV provides numerical examples in order to assess the features and limitations of the proposed approach while comparing it with the method suggested in [13]. Finally, some conclusions are reported in Section V.…”

Section: Introductionmentioning

confidence: 99%

“…The principal interest of such a method is to accurately reconstruct the unknown scatterers without linear approximation and present an efficient recovery algorithm of sparse scatterers by reducing possible ambiguities on the scatterer sparsity deduced from the null values of the equivalent current [1], [12]. This new approach will be compared to our previous work where the nonlinear inverse problem is solved by directly enforcing the sparsity on the unknown contrast [13].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sparse reconstruction algorithms for nonlinear microwave imaging

Zaimaga¹,

Fraysse²,

Lambert³

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents a two-step inverse process which allows sparse recovery of the unknown (complex) dielectric profiles of scatterers for nonlinear microwave imaging. The proposed approach is applied to a nonlinear inverse scattering problem arising in microwave imaging and correlated with joint sparsity which gives multiple sparse solutions that share a common nonzero support. Numerical results demonstrate the potential of the proposed two step inversion approach when compared to existing sparse recovery algorithm for the case of small scatterers.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Two-step Inversion Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparse reconstruction algorithms for nonlinear microwave imaging

Zaimaga¹,

Fraysse²,

Lambert³

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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show abstract

“…Sparsity has been widely investigated and incorporated within classical computational methods in order to solve ISPs [22]. In [23], the fast iterative shrinkage-thresholding algorithm (FISTA) [24] is combined with a modified gradient method (MGM) to enforce the sparsity in both non-sparse and sparse domain, where the discrete wavelet transform (DWT) [25] is employed to reach a sparse representation of the unknown.…”

Section: Introductionmentioning

confidence: 99%

Group Sparsity Penalized Contrast Source Solution Method for 2-D Non-Linear Inverse Scattering

Zhang

Lambert

Fraysse

et al. 2022

IEEE Open J. Antennas Propag.

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A group sparsity penalized CSI in the wavelet domain is proposed to alleviate ill-posedness within the framework of a contrast-source inversion (CSI) method. It is then applied to the retrieval of a large inhomogeneous dielectric scatterer from time-harmonic single-frequency data. As dependency exists between wavelet coefficients at different scales, referred to as the parent-child relationship, it enables to yield the wavelet quad tree structure. Therefore, wavelet coefficients can be regarded not only as pixel-wise sparse, but also group-wise sparse. Focus is put on using the dual-tree complex wavelet transform (CWT) to properly achieve the sought-after group-wise sparse representation of the spatial distribution of the contrast. It provides a 2,1 norm which is added to the standard cost functional to enforce group sparsity onto the wavelet coefficients of the spatially-varying contrast. The replication strategy is combined with the proximal method in order to solve the overlapping group penalized problem. Simulations from synthetic data in different configurations with in particular different signal-to-noise ratios illustrate pros and cons of the proposed method. The approach is shown to overcome the standard CSI method in demanding situations. Comparisons with the discrete wavelet transform (DWT) as usually performed and the 1 norm confirm the advantage of the proposed methodology.

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Sparsity-enforced microwave inverse scattering using soft shrinkage thresholding

Cited by 2 publications

References 17 publications

Sparse reconstruction algorithms for nonlinear microwave imaging

Sparse reconstruction algorithms for nonlinear microwave imaging

Group Sparsity Penalized Contrast Source Solution Method for 2-D Non-Linear Inverse Scattering

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