Adaptive Thresholding for Sparse Image Reconstruction [PDF]
Telfor Journal, 2023 The performance of the class of sparse reconstruction algorithms which is based on the iterative thresholding is highly dependent on a selection of the appropriate threshold value, controlling a trade-off between the algorithm execution time and the ...
I. Volaric, V. Sucic
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Element-Wise Adaptive Thresholds for Learned Iterative Shrinkage Thresholding Algorithms
IEEE Access, 2020 In this paper, we propose element-wise adaptive threshold methods for learned iterative shrinkage thresholding algorithms. The threshold for each element is adapted in such a way that it is set to be smaller when the previously recovered estimate or the ...
Dohyun Kim, Daeyoung Park
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A novel method for transformer fault diagnosis based on refined deep residual shrinkage network
IET Electric Power Applications, 2022 This study proposes a novel method to improve the fault identification performance of transformers. First, to couple multiple factors, a high‐dimensional feature map composed of the feature gas concentrations and some associated variables is constructed.
Hao Hu, Xin Ma, Yizi Shang
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Learning-based accelerated sparse signal recovery algorithms
ICT Express, 2021 In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method ...
Dohyun Kim, Daeyoung Park
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Iterative Soft/Hard Thresholding with Homotopy Continuation for Sparse Recovery [PDF]
, 2017 In this note, we analyze an iterative soft / hard thresholding algorithm with homotopy continuation for recovering a sparse signal $x^\dag$ from noisy data of a noise level $\epsilon$.
Jiao, Yuling, Jin, Bangti, Lu, Xiliang
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Image Denoising by Deep Convolution Based on Sparse Representation
Computers, 2023 Noise filtering is a crucial task in digital image processing, performing the function of preprocessing. In this paper, we propose an algorithm that employs deep convolution and soft thresholding iterative algorithms to extract and learn the features of ...
Shengqin Bian +3 more
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Distributed soft thresholding for sparse signal recovery [PDF]
, 2013 In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a ...
Fosson, Sophie M. +2 more
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Improved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models
EURASIP Journal on Advances in Signal Processing, 2018 In this paper, we propose a new method for support detection and estimation of sparse and approximately sparse signals from compressed measurements. Using a double Laplace mixture model as the parametric representation of the signal coefficients, the ...
Chiara Ravazzi, Enrico Magli
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Iterative Deblending of off-the-Grid Simultaneous Source Data
IEEE Access, 2021 Simultaneous source acquisition can enhance the seismic data quality or improve the field acquisition efficiency. However, one of the disadvantages is that the simultaneous source data are often obtained on a non-uniform sampled grid in realistic ...
Hua Zhang +3 more
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On the performance of algorithms for the minimization of $\ell_1$-penalized functionals [PDF]
, 2009 The problem of assessing the performance of algorithms used for the minimization of an $\ell_1$-penalized least-squares functional, for a range of penalty parameters, is investigated.
Beck A +6 more
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