Abstract

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 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {C}$ </tex-math></inline-formula> WT) to properly achieve the sought-after group-wise sparse representation of the spatial distribution of the contrast. It provides a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> norm confirm the advantage of the proposed methodology.

Highlights

  • I Nverse scattering problems (ISPs) [1] are about the retrieval of the characteristics of an unknown or partially unknown scatterer, such as its geometry or the distribution of its physical parameters, from the knowledge of the fields it scatters when probed by known sources

  • In [36], the commonly used discrete wavelet transform (DWT) offers a sparse representation of the profile to be reconstructed, the linearized inverse scattering problem within the first-order Born approximation framework is solved with the Bayesian version Compressive sensing (CS)-based procedure

  • It has been proved in [52] that the near shift invariance of the dual-tree complex wavelet transform (CWT) leads to better persistence of the magnitudes around the edgy regions, which ensures stronger dependence in inter-scale neighborhoods, in other words, which strengthens the relationship between parent and children wavelet coefficients

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Summary

INTRODUCTION

I Nverse scattering problems (ISPs) [1] are about the retrieval of the characteristics of an unknown or partially unknown scatterer, such as its geometry or the distribution of its physical parameters, from the knowledge of the fields it scatters when probed by known sources (the calculation of such fields results from the solution of a direct scattering problem). Iterative shrinkage thresholding algorithm (ISTA) [24] is implemented within the framework of a boundary integral method in [26] to solve the optimization problem with zeroth/first-norm penalty term. In [36], the commonly used DWT offers a sparse representation of the profile to be reconstructed, the linearized inverse scattering problem within the first-order Born approximation framework is solved with the Bayesian version CS-based procedure. Preliminary bricks of the analysis are found in summaries of conferences [43] and [44], the first with focus on the multiscale wavelet-based method, the second with focus on how soft-thresholding can help to sparsify the reconstruction

FORWARD PROBLEM
INVERSION ALGORITHM
Sparsity inducing norms
Dual-tree complex wavelet transform
Contrast source inversion
17: Update hyperparameters
Reconstruction of the synthetic Austria profile
CONCLUSION
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