Suchergebnisse - Monotone fast iterative shrinkage–thresholding algorithm
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Accelerating monotone fast iterative shrinkage–thresholding algorithm with sequential subspace optimization for sparse recovery
ISSN: 1863-1703, 1863-1711Veröffentlicht: London Springer London 01.06.2020Veröffentlicht in Signal, image and video processing (01.06.2020)“… The present paper focuses on accelerating monotone fast iterative shrinkage–thresholding algorithm (MFISTA …”
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New over-relaxed monotone fast iterative shrinkage-thresholding algorithm for linear inverse problems
ISSN: 1751-9659, 1751-9667Veröffentlicht: The Institution of Engineering and Technology 12.12.2019Veröffentlicht in IET image processing (12.12.2019)“… The over-relaxed monotone fast iterative shrinkage-thresholding algorithm (OMFISTA) needs to satisfy a complex convergence condition with respect to its additional parameters …”
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Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions
ISSN: 1057-7149, 1941-0042, 1941-0042Veröffentlicht: United States IEEE 01.07.2017Veröffentlicht in IEEE transactions on image processing (01.07.2017)“… Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA …”
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Adaptive monotone fast iterative shrinkage thresholding algorithm for fluorescence molecular tomography
ISSN: 1751-8822, 1751-8830Veröffentlicht: The Institution of Engineering and Technology 01.08.2015Veröffentlicht in IET science, measurement & technology (01.08.2015)“… To obtain a solution to such an optimisation problem, an innovative version of the traditional over-relaxation algorithm was proposed by including additional procedures for updating the step size …”
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Some modified fast iterative shrinkage thresholding algorithms with a new adaptive non-monotone stepsize strategy for nonsmooth and convex minimization problems
ISSN: 0926-6003, 1573-2894Veröffentlicht: New York Springer US 01.11.2022Veröffentlicht in Computational optimization and applications (01.11.2022)“… The “ fast iterative shrinkage-thresholding algorithm " (FISTA) is one of the most famous first order optimization schemes, and the stepsize, which plays …”
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High-resolution sonar imaging based on wideband fast iterative shrinkage thresholding deconvolution beamforming
ISSN: 1520-8524, 1520-8524Veröffentlicht: United States 01.11.2025Veröffentlicht in The Journal of the Acoustical Society of America (01.11.2025)“… In response, this study presents a matrix transformation and monotone fast iterative shrinkage thresholding algorithm (MFISTA …”
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A temperature field reconstruction method based on acoustic thermometry
ISSN: 0263-2241, 1873-412XVeröffentlicht: Elsevier Ltd 15.08.2022Veröffentlicht in Measurement : journal of the International Measurement Confederation (15.08.2022)“… •A new method of temperature field reconstruction is proposed.•The proposed method solves the problem of missing edge temperature information.•Reconstruction …”
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
ISSN: 1936-4954, 1936-4954Veröffentlicht: Philadelphia Society for Industrial and Applied Mathematics 01.01.2009Veröffentlicht in SIAM journal on imaging sciences (01.01.2009)“… However, such methods are also known to converge quite slowly. In this paper we present a new fast iterative shrinkage-thresholding algorithm (FISTA …”
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A Generalized Accelerated Proximal Gradient Approach for Total-Variation-Based Image Restoration
ISSN: 1057-7149, 1941-0042, 1941-0042Veröffentlicht: New York, NY IEEE 01.10.2011Veröffentlicht in IEEE transactions on image processing (01.10.2011)“… As compared with the recently developed APG-based methods for TV-based image restoration, i.e., monotone version of the two-step iterative shrinkage/thresholding algorithm …”
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A Fast Iterative Shrinkage/Thresholding Algorithm via Laplace Norm for Sound Source Identification
ISSN: 2169-3536, 2169-3536Veröffentlicht: Piscataway IEEE 2020Veröffentlicht in IEEE access (2020)“… To improve the resolution quality of traditional ESM in the middle-frequency band and the high-frequency band, we introduced a fast iterative shrinkage/thresholding algorithm via Laplace norm …”
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An improved fast iterative shrinkage thresholding algorithm with an error for image deblurring problem
ISSN: 2730-5422, 2730-5422Veröffentlicht: Cham Springer International Publishing 01.11.2021Veröffentlicht in Fixed point theory and algorithms for sciences and engineering (01.11.2021)“… We also apply our main result to improve the fast iterative shrinkage thresholding algorithm (IFISTA …”
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Smoothing and Decomposition for Analysis Sparse Recovery
ISSN: 1053-587X, 1941-0476Veröffentlicht: New York, NY IEEE 01.04.2014Veröffentlicht in IEEE transactions on signal processing (01.04.2014)“… We consider the use of a monotone version of the fast iterative shrinkage-thresholding algorithm (MFISTA …”
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Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging
ISSN: 2573-0436, 2333-9403, 2333-9403Veröffentlicht: United States IEEE 01.03.2019Veröffentlicht in IEEE transactions on computational imaging (01.03.2019)“… An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed in this paper …”
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Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
ISSN: 1057-7149, 1941-0042, 1941-0042Veröffentlicht: New York, NY IEEE 01.11.2009Veröffentlicht in IEEE transactions on image processing (01.11.2009)“… To achieve this task, we combine an acceleration of the well known dual approach to the denoising problem with a novel monotone version of a fast iterative shrinkage/thresholding algorithm (FISTA …”
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Accelerating the over-relaxed iterative shrinkage-thresholding algorithms with fast and exact line search for high resolution tomographic image reconstruction
Veröffentlicht: IEEE 01.09.2015Veröffentlicht in 2015 IEEE International Conference on Image Processing (ICIP) (01.09.2015)“… This paper proposes an accelerating process through the use of a fast and exact line search for the over-relaxed monotone fast iterative shrinkage-threshold algorithms (OMFISTA …”
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Mixed Higher Order Variational Model for Image Recovery
ISSN: 1024-123X, 1563-5147Veröffentlicht: New York Hindawi Publishing Corporation 01.01.2014Veröffentlicht in Mathematical problems in engineering (01.01.2014)“… Due to the equivalent formulation of the proposed regularizer, an efficient fast projected gradient algorithm combined with monotone fast iterative shrinkage thresholding, called, FPG-MFISTA …”
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Robustifying stability of the Fast iterative shrinkage thresholding algorithm for \ell regularized problems
ISSN: 2076-1465Veröffentlicht: EURASIP 23.08.2021Veröffentlicht in 2021 29th European Signal Processing Conference (EUSIPCO) (23.08.2021)“… The fast iterative shrinkage-thresholding algorithm (FISTA) is a well-known first order method used to minimize \ell_{1 …”
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Iterative image reconstruction algorithm analysis for optical CT radiochromic gel dosimetry
ISSN: 2057-1976, 2057-1976Veröffentlicht: England IOP Publishing 01.05.2024Veröffentlicht in Biomedical physics & engineering express (01.05.2024)“… ) and iterative techniques are required. To compare a subset of the top performing algorithms in terms of image …”
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Improving "Fast Iterative Shrinkage-Thresholding Algorithm": Faster, Smarter and Greedier
ISSN: 2331-8422Veröffentlicht: Ithaca Cornell University Library, arXiv.org 20.01.2021Veröffentlicht in arXiv.org (20.01.2021)“… The "fast iterative shrinkage-thresholding algorithm", a.k.a. FISTA, is one of the most well-known first-order optimisation scheme in the literature, as it achieves the worst-case \(O(1/k^2 …”
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An Iterative Reweighted Method for Tucker Decomposition of Incomplete Tensors
ISSN: 1053-587X, 1941-0476Veröffentlicht: New York IEEE 15.09.2016Veröffentlicht in IEEE transactions on signal processing (15.09.2016)“… We consider the problem of low-rank decomposition of incomplete tensors. Since many real-world data lie on an intrinsically low-dimensional subspace, tensor …”
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