Entropy-Regularized Iterative Weighted Shrinkage-Thresholding Algorithm (ERIWSTA) for inverse problems in imaging. by Limin Ma, Bingxue Wu, Yudong Yao, Yueyang Teng

“…The iterative shrinkage-thresholding algorithm (ISTA) is a classic optimization algorithm for solving ill-posed linear inverse problems. …”
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High-resolution surface wave dispersion spectrum computation based on iterative threshold shrinkage algorithm and its application to irregularly sampled data by Luo Li, Luo Li, Xuri Huang, Xuri Huang, Weiping Cao, Hai Yao, Hai Yao, Yungui Xu, Yungui Xu, Huatao Zhao, Huatao Zhao, Bin Wu

“…This paper presents a high-resolution method for surface-wave dispersion spectrum computation using Tau-P transform implemented with an iterative threshold shrinkage algorithm scheme. In this method, Tau-P transform is formulated as a sparse inversion scheme, and the Tau-P coefficients are iteratively thresholded to achieve a high-resolution Tau-P domain representation. …”
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Direction of Arrival (DOA) Estimation Using a Deep Unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) Network in a Non-Uniform Metasurface by Xinyi Niu, Xiaolong Su, Lida He, Guanchao Chen

“…Additionally, a deep unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) network is constructed by transforming Iterative Shrinkage Thresholding Algorithm (ISTA) iterative steps into trainable neural network layers, combining model-driven logic with data-driven parameter optimization. …”
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Convolutional sparse coding network for sparse seismic time-frequency representation by Qiansheng Wei, Zishuai Li, Haonan Feng, Yueying Jiang, Yang Yang, Zhiguo Wang

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Rényi Entropy-Based Shrinkage with RANSAC Refinement for Sparse Time-Frequency Distribution Reconstruction by Vedran Jurdana

“…The Rényi entropy-based two-step iterative shrinkage/thresholding (RTwIST) algorithm addresses this issue by incorporating local component estimates to guide adaptive thresholding, thereby improving interpretability and robustness. …”
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Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

“…Aiming at the problem that the traditional ECT is not accurate in complex situations, this paper proposes a depth learning based inversion method Through the improvement and optimization of the traditional extreme learning machine, the image feature information obtained by the reconstructed image method is used as the training data, and the result obtained by inputting the data into the predictive model is used as the prior information The cost function is used to encapsulate the prior knowledge and domain expertise, and spatial regularizers and time regularizers are introduced to enhance sparsity The separated Bregman (SB) algorithm and the iterative shrinkage threshold (FIST) method are used to solve the specified cost function The final imaging result is obtained The simulation results show that the image reconstructed by this method has less than 10% error compared with the original flow pattern, and reduces artifacts and distortion, which improves the reconstructed image quality…”
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Comparative analysis of FISTA and inertial Tseng algorithm for enhanced image restoration in prostate cancer imaging by Abubakar Adamu, Huzaifa Umar, Samuel Eniola Akinade, Dilber Uzun Ozsahin

“…The Inertial Tseng Algorithm (ITA) and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) are well-established methods that provide effective ways to approximate zeros of the sum of monotone operators. …”
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A Novel Phase Error Estimation Method for TomoSAR Imaging Based on Adaptive Momentum Optimizer and Joint Criterion by Muhan Wang, Silin Gao, Xiaolan Qiu, Zhe Zhang

“…In addition, our approach introduces a joint iterative solution framework, which incorporates a modified accelerated iterative shrinkage-threshold sparse recovery algorithm and an adaptive momentum optimizer for interchannel phase error estimation in a gradient descent manner. …”
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A Sparse Representation-Based Reconstruction Method of Electrical Impedance Imaging for Grounding Grid by Ke Zhu, Donghui Luo, Zhengzheng Fu, Zhihang Xue, Xianghang Bu

“…It constructs constraints using the sparsity of conductivity distribution under a certain sparse basis and utilizes the accelerated Fast Iterative Shrinkage Threshold Algorithm (FISTA) for iterative solutions, aiming to improve the imaging quality and reconstruction accuracy. …”
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Multi-targets device-free localization based on sparse coding in smart city by Min Zhao, Danyang Qin, Ruolin Guo, Guangchao Xu

“…To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. …”
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MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS) by Youhao Yu, Richard M. Dansereau

“…We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. …”
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Gridless DOA Estimation with Extended Array Aperture in Automotive Radar Applications by Pengyu Jiang, Silin Gao, Jie Zhao, Zhe Zhang, Bingchen Zhang

“…By using the Iterative Vandermonde Decomposition and Shrinkage Threshold (IVDST) algorithm, we can achieve fast ANM, which effectively mitigates off-grid errors while reducing reconstruction complexity. …”
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Development of a Japanese polygenic risk score model for amyloid-β PET imaging in Alzheimer’s disease by Misato Kaishima, Junichi Ito, Kentaro Takahashi, Kenji Tai, Junro Kuromitsu, Shogyoku Bun, Daisuke Ito

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Compressive SAR Imaging Based on Modified Low-Rank and Sparse Decomposition by Jeong-Il Byeon, Wookyung Lee, Jihoon Choi

“…For the CS-MLRSD scheme, an iterative thresholding algorithm is derived to separately reconstruct the low-rank and sparse components. …”
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Joint Data Hiding and Partial Encryption of Compressive Sensed Streams by Cristina-Elena Popa, Constantin-Cristian Damian, Daniela Coltuc

“…In our tests, embedding on 10 levels results in ≈18 dB distortion for images of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>256</mn><mo>×</mo><mn>256</mn></mrow></semantics></math></inline-formula> pixels reconstructed with the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). …”
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The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy by Mingyan Zhang, Junfa Sheng

“…Random forest algorithms were used to construct NR and SR models. …”
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Sparsity-Guided Phase Retrieval to Handle Concave- and Convex-Shaped Specimens in Inline Holography, Taking the Complexity Parameter into Account by Yao Koffi, Jocelyne M. Bosson, Marius Ipo Gnetto, Jeremie T. Zoueu

“…This method associates free-space backpropagation with the fast iterative shrinkage-thresholding algorithm (FISTA), which incorporates an improvement in the total variation (TV) to guide the complexity of the phase retrieval solution from the complex diffracted field measurement. …”
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Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules by Xingxing Chen, Xingxing Chen, Lili Zhang, Bin Chen, Jiajia Lu

“…A total of 107 radiomics features were extracted from the US images, and the radiomics score (Rad-score) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. …”
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Multi‐sequence MRI‐based clinical‐radiomics models for the preoperative prediction of microsatellite instability‐high status in endometrial cancer by Zhuang Li, Yi Su, Yongbin Cui, Yong Yin, Zhenjiang Li

“…The intraclass correlation coefficients, Spearman correlation analysis, Mann–Whitney U test, and least absolute shrinkage and selection operator (LASSO) algorithm were employed for feature selection in radiomics models' development. …”
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A Robust Method Based on Deep Learning for Compressive Spectrum Sensing by Haoye Zeng, Yantao Yu, Guojin Liu, Yucheng Wu

“…To overcome these limitations, we propose BEISTA-Net, a deep learning-based framework for reconstructing compressed wideband signals. BEISTA-Net integrates the iterative shrinkage-thresholding algorithm (ISTA) with deep learning, thereby extracting and enhancing the block sparsity features of wideband spectrum signals, which significantly improves reconstruction accuracy. …”
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