Element-Wise Adaptive Thresholds for Learned Iterative Shrinkage Thresholding Algorithms

Kim, Dohyun; Park, Daeyoung

doi:10.1109/access.2020.2978237

Cited by 26 publications

(37 citation statements)

References 19 publications

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“…If T (x, µ, a) is a function which converges to 0 when x gradually becomes larger as the algorithm iterates, the algorithm will converge more quickly [20]. In this paper, we propose an adaptive threshold which is defined below…”

Section: B Improved Adaptive Istamentioning

confidence: 99%

“…In gradient (20), both parameter µ and a determine the sparsity of IA-ISTA, and rate of convergence in IA-ISTA is mainly determined by parameter a. Parameter selection in IA-ISTA is illustrated as follows.…”

Section: B Improved Adaptive Istamentioning

confidence: 99%

“…In order to improve algorithm efficiency of ISTA, a fast iterative shrinkage-thresholding algorithm (FISTA) [17], [18] was proposed, which has been successfully applied in SAR tomography [19]. Besides, element-wise adaptive thresholds for learned ISTA and ISTA with adaptive threshold were analysed in theory [20]. In this paper, we propose an improved adaptive ISTA (IA-ISTA) in 3D image reconstruction of airborne SAR tomography.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient 3D Image Reconstruction of Airborne TomoSAR Based on Back Projection and Improved Adaptive ISTA

et al. 2021

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Airborne SAR tomography (TomoSAR) 3D image reconstruction can be realized with combination of 2D imaging algorithms and compressed sensing (CS) algorithms. However, most typical CS algorithms can not achieve a balance between algorithm efficiency and 3D reconstruction accuracy. Due to difficulties in flight path control of airborne SAR, it is hard to realize registration of SAR images with frequency-domain imaging algorithms because of time-varying baseline. To address these problems, an efficient 3D image reconstruction method for airborne TomoSAR based on back projection (BP) algorithm and improved adaptive iterative shrinkage-thresholding algorithm (IA-ISTA) is proposed. First, 2D images are achieved with BP algorithm on the ground plane. After registration of SAR images, 3D image reconstruction results in the elevation direction are realized with IA-ISTA. Selection criterion of IA-ISTA parameters are given in this paper. At last, final 3D image reconstruction results are achieved after geometrical transformation based on geometric relationship. Both simulated data and measured data of a P-band airborne TomoSAR system are used. 3D image reconstruction results show that the proposed method outperforms traditional methods regarding efficiency and accuracy, which proves the validity and practicality of the proposed method.INDEX TERMS Airborne SAR tomography (TomoSAR), 3D image reconstruction, compressed sensing (CS), iterative shrinkage-thresholding algorithm (ISTA), back projection (BP) algorithm.

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Section: B Improved Adaptive Istamentioning

confidence: 99%

Section: B Improved Adaptive Istamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient 3D Image Reconstruction of Airborne TomoSAR Based on Back Projection and Improved Adaptive ISTA

et al. 2021

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“…The optimization sub-problems given in ( 9 ) and ( 10 ) are solved in closed-form by using the proximal operators. To be more precise, we utilize the element-wise soft-thresholding and the element-wise singular value soft-thresholding (i.e., element-wise soft-thresholding on the singular value of a matrix) [ 38 , 39 ] as given below.

…”

Section: Clutter Suppression For High-q and Low-q Tagsmentioning

confidence: 99%

“…Here,

and

are the element-wise singular value soft-thresholding and element-wise soft-thresholding operators [ 38 , 39 ], respectively.…”

Section: Clutter Suppression For High-q and Low-q Tagsmentioning

confidence: 99%

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Sánchez-Pastor

Thanthrige

Ilgac

et al. 2021

Sensors

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Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks.

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Deep learning for compressive sensing: a ubiquitous systems perspective

Machidon

Pejović

2022

Artif Intell Rev

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Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS–DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS–DL to real world implementations in the ubiquitous computing domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS–DL efficient, outline major trends in the CS–DL research space, and derive guidelines for the future evolution of CS–DL within the ubiquitous computing domain.

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Element-Wise Adaptive Thresholds for Learned Iterative Shrinkage Thresholding Algorithms

Cited by 26 publications

References 19 publications

Efficient 3D Image Reconstruction of Airborne TomoSAR Based on Back Projection and Improved Adaptive ISTA

Efficient 3D Image Reconstruction of Airborne TomoSAR Based on Back Projection and Improved Adaptive ISTA

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Deep learning for compressive sensing: a ubiquitous systems perspective

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