Soft Shrinkage Thresholding Algorithm for Nonlinear Microwave Imaging

Zaimaga, Hidayet; Lambert, Marc

doi:10.1088/1742-6596/756/1/012011

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In this paper, we focus on methods that aim to estimate any permittivity distribution (that is, without any strong prior assumption on the inspected object), based on the exact problem formulation. Previous works based on multiplicative [33] or additive [13], edge-preserving or sparsity-enhancing [41], regularization fall within this category. All these methods rely on repeated computations of forward scattering problems.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Microwave Imaging: Fast and Accurate Computations with Block Resolution Algorithms

Friedrich

Bourguignon

Idier

et al. 2020

Sensors

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This paper considers the microwave imaging reconstruction problem, based on additive penalization and gradient-based optimization. Each evaluation of the cost function and of its gradient requires the resolution of as many high-dimensional linear systems as the number of incident fields, which represents a large amount of computations. Since all such systems involve the same matrix, we propose a block inversion strategy, based on the block-biconjugate gradient stabilized (BiCGStab) algorithm, with efficient implementations specific to the microwave imaging context. Numerical experiments performed on synthetic data and on real measurements show that savings in computing time can reach a factor of two compared to the standard, sequential, BiCGStab implementation. Improvements brought by the block approach are even more important for the most difficult reconstruction problems, that is, with high-frequency illuminations and/or highly contrasted objects. The proposed reconstruction strategy is shown to achieve satisfactory estimates for objects of the Fresnel database, even on the most contrasted ones.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Microwave Imaging: Fast and Accurate Computations with Block Resolution Algorithms

Friedrich

Bourguignon

Idier

et al. 2020

Sensors

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Target Tracking via Particle Filter and Convolutional Network

Chu

Wang

Xing

2018

Journal of Electrical and Computer Engineering

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We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.

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Soft Shrinkage Thresholding Algorithm for Nonlinear Microwave Imaging

Cited by 2 publications

References 13 publications

Three-Dimensional Microwave Imaging: Fast and Accurate Computations with Block Resolution Algorithms

Three-Dimensional Microwave Imaging: Fast and Accurate Computations with Block Resolution Algorithms

Target Tracking via Particle Filter and Convolutional Network

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