Non-Linear Inverse Scattering via Sparsity Regularized Contrast Source Inversion

Bevacqua, Martina T.; Crocco, Lorenzo; Donato, Loreto Di; Isernia, Tommaso

doi:10.1109/tci.2017.2675708

Cited by 41 publications

(27 citation statements)

References 48 publications

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“…We now compare our method CISOR with several stateof-the-art methods, including iterative linearization (IL) [29], [30], contrast sourse inversion (CSI) [31]- [33], and SEAGLE [40], as well as a conventional linear method, the first Born approximation (FB) [1]. All algorithms use additive total variation regularization.…”

Section: Resultsmentioning

confidence: 99%

Accelerated Image Reconstruction for Nonlinear Diffractive Imaging

Mansour

Liu

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications. While the most popular formulations of the problem are based on linearizing the object-light relationship, there is an increased interest in considering nonlinear formulations that can account for multiple light scattering. In this paper, we propose an image reconstruction method, called CISOR, for nonlinear diffractive imaging, based on a nonconvex optimization formulation with total variation (TV) regularization.The nonconvex solver used in CISOR is our new variant of fast iterative shrinkage/thresholding algorithm (FISTA). We provide fast and memory-efficient implementation of the new FISTA variant and prove that it reliably converges for our nonconvex optimization problem. In addition, we systematically compare our method with other state-of-the-art methods on simulated as well as experimentally measured data in both 2D and 3D settings.

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Section: Resultsmentioning

confidence: 99%

Accelerated Image Reconstruction for Nonlinear Diffractive Imaging

Mansour

Liu

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In fact, a necessary (still not sufficient) condition to overcome ill-posedness is that the dimension of the space where the unknown function is looked for is not greater than the one of the data space [21]. Some examples can be found in [21,[31][32][33][34], wherein projection methods based on Fourier harmonics or Wavelet transform are adopted.…”

Section: Inverse Scattering Problemmentioning

confidence: 99%

“…For the above circumstances, in the following a CSI scheme has been adopted. In particular, the unknown χ and the auxiliary one W are simultaneously estimated by minimizing a cost functional [18,32], which considers both the misfit in the data Eq. (1) and the one in the state Eq.…”

Section: New Estimation Of Eps Via Mri/ct Based Projectionmentioning

confidence: 99%

A Method for Effective Permittivity and Conductivity Mapping of Biological Scenarios via Segmented Contrast Source Inversion

Bevacqua¹,

Bellizzi²,

Isernia³

et al. 2019

PIER

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Quantitative estimation of both conductivity and permittivity of biological tissues is essential in many biomedical applications, ranging from therapeutic treatments to safety assessment of medical devices and dosimetry. Typically, the electromagnetic field distribution inside the body is predicted based on available ex-vivo measured electrical properties. Unfortunately, these values may be quite different from the ones measured in-vivo and cannot account for the differences among individuals. As a result, their use can introduce significant errors affecting therapeutic treatments and dose estimation. To cope with this problem, in this paper a new approach for estimation of effective electrical properties of human tissues is introduced. The proposed strategy is based on the solution of an inverse scattering problem (by means of a contrast source inversion scheme) and the use of an effective representation of the unknowns based on spatial priors derived by magnetic resonance imaging or computed tomography. The approach is tested in controlled conditions against simulated single frequency data and realistic and anthropomorphic head and neck phantoms. Moreover, the inherent advantages have been assessed in the framework of hyperthermia treatment planning.

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“…In recent years, there has been wide application of compressed sensing technology in information [12], image optimization [13], health care [14,15], inverse scattering problems [16,17] and other fields. Due to the limitation of acquisition conditions, the phenomenon of traces missing in prestack seismic data is severe.…”

Section: Introductionmentioning

confidence: 99%

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

Dong

et al. 2020

Applied Sciences

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Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) combined EMD_CS_ST denoising method is proposed. First, through EMD decomposition of the noise signal, a series of Intrinsic Mode Functions (IMF) from high frequency to low frequency are obtained. By calculating the correlation coefficient between each IMF and the original signal, the boundary component between the signal and the noise was identified, and the boundary component and its previous components were sparsely processed in the discrete wavelet transform domain to obtain the original sparse coefficient θ. Second, θ is filtered by ST to get the reconstruction coefficient θnew after denoising. Then, CS was used to recover and reconstruct θnew to get the denoised IMFnew component and then recombined with the remaining IMF components to get the signal after denoising. In the simulation experiment, the denoising process of EMD_CS_ST method is introduced in detail, and the denoising ability of EMD_CS_ST, DWT, EEMD, and VMD_DWT under 10 different noise levels is discussed. The signal-to-noise ratio, signal standard deviation, correlation coefficient, waveform diagram, and spectrogram before and after denoising are compared and analyzed. Moreover, the signals obtained from the underground cavern of the Shuangjiangkou hydropower station were denoised by the EMD_CS_ST method, and the signals before and after denoising were analyzed by time-frequency spectrum. These results show that the proposed method has better denoising ability.

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Non-Linear Inverse Scattering via Sparsity Regularized Contrast Source Inversion

Cited by 41 publications

References 48 publications

Accelerated Image Reconstruction for Nonlinear Diffractive Imaging

Accelerated Image Reconstruction for Nonlinear Diffractive Imaging

A Method for Effective Permittivity and Conductivity Mapping of Biological Scenarios via Segmented Contrast Source Inversion

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

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