Relevant bibliographies by topics / Reweighted l1 minimization

Contents

  1. Journal articles
  2. Book chapters
  3. Conference papers

Academic literature on the topic 'Reweighted l1 minimization'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Reweighted l1 minimization.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Reweighted l1 minimization"

1

Wu, Jiayong, Jin Mao, and Jiawei Cao. "Improved iterative reweighted L1 norm minimization method for sound source identification." Vibroengineering Procedia 58 (May 15, 2025): 178–84. https://doi.org/10.21595/vp.2025.24944.

Full text
Abstract:
Sparse reconstruction algorithm is one of the main research topics in compressed sensing. To address the shortcomings of existing iteratively reweighted l1-norm minimization methods, which exhibit poor performance in low-frequency sound source identification and weak anti-interference capability, this paper proposes an improved iteratively reweighted l1-norm minimization method. Unlike traditional methods, this method introduces a log-sum penalty function and constructs a surrogate function, transforming the problem into an effective form for solving the source strength distribution vector. Th
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Shuanghui, Yongxiang Liu, Xiang Li, and Dewen Hu. "Enhancing ISAR Image Efficiently via Convolutional Reweighted l1 Minimization." IEEE Transactions on Image Processing 30 (2021): 4291–304. http://dx.doi.org/10.1109/tip.2021.3070442.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kim, Hojin, Stephen Becker, Rena Lee, et al. "Improving IMRT delivery efficiency with reweighted L1-minimization for inverse planning." Medical Physics 40, no. 7 (2013): 071719. http://dx.doi.org/10.1118/1.4811100.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Tran, T. T. T., C. T. Pham, H. P. Dang, A. Kopylov, V. H. Mai, and T. X. L. Nguyen. "NON-CONVEX HYBRID TOTAL VARIATION FOR RESTORING MEDICAL IMAGE CORRUPTED BY POISSON NOISE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 255–60. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-255-2023.

Full text
Abstract:
Abstract. In this work, we proposed the hybrid non-convex regularizers for Poisson noise removal on medical images. The model is built by a combination of non-convex total variation and non-convex fractional total variation. The proposed model allows for avoiding the annoying staircase artifacts and obtaining the reconstruction results with sharp and neat edges during the noise removal process. For handling the minimization problem, we employ the alternating minimization method associated with the iteratively reweighted l1 algorithm. Numerical experiments illustrate the efficiency of the propo
APA, Harvard, Vancouver, ISO, and other styles
5

Li, Zhong-Xiao, and Zhen-Chun Li. "Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction." GEOPHYSICS 83, no. 2 (2018): V99—V113. http://dx.doi.org/10.1190/geo2016-0384.1.

Full text
Abstract:
After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational co
APA, Harvard, Vancouver, ISO, and other styles
6

Zhang, Yilong, Yuehua Li, Shujin Zhu, and Yuanjiang Li. "A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field." Sensors 15, no. 10 (2015): 24945–60. http://dx.doi.org/10.3390/s151024945.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hand, Paul, and Babhru Joshi. "A convex program for mixed linear regression with a recovery guarantee for well-separated data." Information and Inference: A Journal of the IMA 7, no. 3 (2018): 563–79. http://dx.doi.org/10.1093/imaiai/iax018.

Full text
Abstract:
Abstract We introduce a convex approach for mixed linear regression over d features. This approach is a second-order cone program, based on L1 minimization, which assigns an estimate regression coefficient in $\mathbb {R}^{d}$ for each data point. These estimates can then be clustered using, for example, k-means. For problems with two or more mixture classes, we prove that the convex program exactly recovers all of the mixture components in the noiseless setting under technical conditions that include a well-separation assumption on the data. Under these assumptions, recovery is possible if ea
APA, Harvard, Vancouver, ISO, and other styles
8

Kim, Hojin, Josephine Chen, Adam Wang, Cynthia Chuang, Mareike Held, and Jean Pouliot. "Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction." Physics in Medicine and Biology 61, no. 18 (2016): 6878–91. http://dx.doi.org/10.1088/0031-9155/61/18/6878.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Aslan, Yanki, Jan Puskely, and Alexander Yarovoy. "Heat source layout optimization for two-dimensional heat conduction using iterative reweighted L1-norm convex minimization." International Journal of Heat and Mass Transfer 122 (July 2018): 432–41. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2018.02.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Shoudong, and Xiaohong Chen. "Absorption-compensation method by l1-norm regularization." GEOPHYSICS 79, no. 3 (2014): V107—V114. http://dx.doi.org/10.1190/geo2013-0206.1.

Full text
Abstract:
Absorption in subsurface media greatly reduces the resolution of seismic data. Absorption not only dissipates the high-frequency components of the wave, but it also distorts the seismic wavelet. The inverse [Formula: see text]-filtering method is an effective method to correct the attenuation and dispersion of the seismic wave. We evaluated an absorption compensation method based on [Formula: see text]-norm regularization. Forward modeling in the absorption medium is described by a Fredholm integral equation in the time domain, and the absorption compensation comes down to solving the Fredholm
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Reweighted l1 minimization"

1

Kim, Hwa-Young, Rae-Hong Park, and Ji-Eun Lee. "Image Representation Using a Sparsely Sampled Codebook for Super-Resolution." In Research Developments in Computer Vision and Image Processing. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4558-5.ch001.

Full text
Abstract:
In this chapter, the authors propose a Super-Resolution (SR) method using a vector quantization codebook and filter dictionary. In the process of SR, we use the idea of compressive sensing to represent a sparsely sampled signal under the assumption that a combination of a small number of codewords can represent an image patch. A low-resolution image is obtained from an original high-resolution image, degraded by blurring and down-sampling. The authors propose a resolution enhancement using an alternative l1 norm minimization to overcome the convexity of l0 norm and the sparsity of l1 norm at t
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Reweighted l1 minimization"

1

Needell, Deanna. "Noisy signal recovery via iterative reweighted L1-minimization." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5470154.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Wang, Miao miao, Xing hao Ding, Quan Xiao, and Cong bo Cai. "Contourlet Based MR Image Reconstruction via Reweighted L1-Minimization." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5305606.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wu, Jwo-Yuh, Liang-Chi Huang, Ming-Hsun Yang, Ling-Hua Chang, and Chun-Hung Liu. "Enhanced Noisy Sparse Subspace Clustering via Reweighted L1-Minimization†." In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2018. http://dx.doi.org/10.1109/mlsp.2018.8517025.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Hui, Xiaolin Huang, Yipeng Liu, Sabine Van Huffel, and Qun Wan. "Binary Reweighted l1-Norm Minimization for One-Bit Compressed Sensing." In International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005208802060210.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lee, Hyukjung, Joohwan Chun, and Sungchan Song. "Forward-looking super-resolution radar imaging via reweighted L1-minimization." In 2018 IEEE Radar Conference (RadarConf18). IEEE, 2018. http://dx.doi.org/10.1109/radar.2018.8378601.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Matsushita, Ryosuke, and Toshiyuki Tanaka. "Critical compression ratio of iterative reweighted l1 minimization for compressed sensing." In 2011 IEEE Information Theory Workshop (ITW). IEEE, 2011. http://dx.doi.org/10.1109/itw.2011.6089520.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yang, Yuhua, Wei-Ping Zhu, and Dalei Wu. "Design of sparse FIR filters based on reweighted l1 -norm minimization." In 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251998.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ching-Hua Chang and Jim Ji. "Improved compressed sensing MRI with multi-channel data using reweighted l1 minimization." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5627890.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhengguang, Xie, Li Hongjun, and Li Yunhua. "An efficient iteratively reweighted L1-minimization for image reconstruction from compressed sensing." In 3rd International Conference on Multimedia Technology(ICMT-13). Atlantis Press, 2013. http://dx.doi.org/10.2991/icmt-13.2013.36.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Endra, Oey, and Dadang Gunawan. "Comparison of l1-Minimization and Iteratively Reweighted least Squares-l p-Minimization for Image Reconstruction from Compressive Sensing." In 2010 Second International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT). IEEE, 2010. http://dx.doi.org/10.1109/act.2010.31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Reweighted l1 minimization' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography