A Gradient-thresholding Algorithm for Sparse Regularization
में बचाया:
| में प्रकाशित: | arXiv.org (Jun 4, 2020), p. n/a |
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| मुख्य लेखक: | |
| प्रकाशित: |
Cornell University Library, arXiv.org
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full text outside of ProQuest |
| टैग: |
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| LEADER | 00000nab a2200000uu 4500 | ||
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| 001 | 2410534400 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2410534400 | ||
| 045 | 0 | |b d20200604 | |
| 100 | 1 | |a Nayak, Abinash | |
| 245 | 1 | |a A Gradient-thresholding Algorithm for Sparse Regularization | |
| 260 | |b Cornell University Library, arXiv.org |c Jun 4, 2020 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Inverse problems arise in a wide spectrum of applications in fields ranging from engineering to scientific computation. Connected with the rise of interest in inverse problems is the development and analysis of regularization methods, such as Tikhonov-type regularization methods or iterative regularization methods, which are a necessity in most of the inverse problems. In the last few decades, regularization methods motivating sparsity has been the focus of research, due to the high dimensionalty of the real-life data, and \(\mathcal{L}^1\)-regularization methods (such as LASSO or FISTA) has been in its center (due to their computational simplicity). In this paper we propose a new (semi-) iterative regularization method which is not only simpler than the mentioned algorithms but also yields better results, in terms of accuracy and sparsity of the recovered solution. Furthermore, we also present a very effective and practical stopping criterion to choose an appropriate regularization parameter (here, it's iteration index) so as to recover a regularized (sparse) solution. To illustrate the computational efficiency of this algorithm we apply it to numerically solve the image deblurring problem and compare our results with certain standard regularization methods, like total variation, FISTA, LSQR etc. | |
| 653 | |a Sparsity | ||
| 653 | |a Regularization methods | ||
| 653 | |a Regularization | ||
| 653 | |a Algorithms | ||
| 653 | |a Inverse problems | ||
| 653 | |a Iterative methods | ||
| 773 | 0 | |t arXiv.org |g (Jun 4, 2020), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2410534400/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2006.03437 |