Relevant bibliographies by topics / Compressive Sensing; Sparsity; L1-Norm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Compressive Sensing; Sparsity; L1-Norm'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "Compressive Sensing; Sparsity; L1-Norm"

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Abhilasha, Sharma. "COMPRESSIVE SENSING." INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH 5, no. 2 :SE (2018): 249–59. https://doi.org/10.5281/zenodo.1202511.

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Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality. Since, in conventional system all signals follow the Nyquist criteria, in which the sampling rate must be at least twice the maximum frequency of modulating signal. But, in this new concept we can recover the signal below the Nyquist rate. This paper presents the basic concept of compressive sensing and area of application
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Zhang, Bin, Liuliu Wang, Shuang Li, Futai Xie, and Lideng Wei. "Airborne Single-Pass Multi-Baseline InSAR Layover Separation Method Based on Multi-Look Compressive Sensing." Applied Sciences 12, no. 24 (2022): 12658. http://dx.doi.org/10.3390/app122412658.

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Due to the small number of baselines (2–3), the traditional L1 norm compressive sensing method for layover solution in InSAR has poor separation ability and height estimation stability and a long operation time. This paper, based on the idea of multi-look, adopts a multi-look compressive sensing method and a multi-look compressive sensing method based on separable approximate sparse reconstruction. The layover separation method based on multi-look compressive sensing adopts the surrounding pixels around the current point as independent observations together with this point to increase the obse
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WANG, YANFEI, CHANGCHUN YANG, and JINGJIE CAO. "ON TIKHONOV REGULARIZATION AND COMPRESSIVE SENSING FOR SEISMIC SIGNAL PROCESSING." Mathematical Models and Methods in Applied Sciences 22, no. 02 (2012): 1150008. http://dx.doi.org/10.1142/s0218202511500084.

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Using compressive sensing and sparse regularization, one can nearly completely reconstruct the input (sparse) signal using limited numbers of observations. At the same time, the reconstruction methods by compressing sensing and optimizing techniques overcome the obstacle of the number of sampling requirement of the Shannon/Nyquist sampling theorem. It is well known that seismic reflection signal may be sparse, sometimes and the number of sampling is insufficient for seismic surveys. So, the seismic signal reconstruction problem is ill-posed. Considering the ill-posed nature and the sparsity of
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Ping, Guoli, Zhigang Chu, and Yang Yang. "Compressive Spherical Beamforming for Acoustic Source Identification." Acta Acustica united with Acustica 105, no. 6 (2019): 1000–1014. http://dx.doi.org/10.3813/aaa.919406.

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This study examines a compressive spherical beamforming (CSB) method, using a rigid spherical microphone array to localize and quantify the acoustic contribution of sources. The method relies on the array signal model in the spherical harmonics domain that can be represented as a spatially sparse problem. This makes it possible to use compressive sensing to solve an underdetermined problem via promoting sparsity. The estimation of the angular position of sources with respect to the microphone array, as well as the three-dimensional localization over a volume are investigated. Several sparse re
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Xue, Jize, Yongqiang Zhao, Wenzhi Liao, and Jonathan Chan. "Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction." Remote Sensing 11, no. 2 (2019): 193. http://dx.doi.org/10.3390/rs11020193.

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Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR)
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N, Susithra, Rajalakshmi K, and Ashwath P. "Performance analysis of compressive sensing and reconstruction by LASSO and OMP for audio signal processing applications." Scientific Temper 14, no. 01 (2023): 222–26. http://dx.doi.org/10.58414/scientifictemper.2023.14.1.28.

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Audio signal processing is used in acoustic IoT sensor nodes which have limitations in data storage, computation speed, hardware size and power. In most audio signal processing systems, the recovered data constitutes far less fraction of the sampled data providing scope for compressive sensing (CS) as an efficient way for sampling and signal recovery. Compressive sensing is a signal processing technique in which a sparse approximated signal is reconstructed at the receiving node by a signal recovery algorithm, using fewer samples compared to traditional sampling methods. It has two main stages
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Gao, Rui, Yingyou Wen, and Hong Zhao. "Secure Data Fusion in Wireless Multimedia Sensor Networks via Compressed Sensing." Journal of Sensors 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/636297.

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The paper proposes a novel secure data fusion strategy based on compressed image sensing and watermarking; namely, the algorithm exploits the sparsity in the image encryption. The approach relies onl1-norm regularization, common in compressive sensing, to enhance the detection of sparsity over wireless multimedia sensor networks. The resulting algorithms endow sensor nodes with learning abilities and allow them to learn the sparse structure from the still image data, and also utilize the watermarking approach to achieve authentication mechanism. We provide the total transmission volume and the
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Liu, Beiyi, Guan Gui, Shin-ya Matsushita, and Li Xu. "Compressive Sensing-Based Adaptive Sparse Multipath Channel Estimation." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 1 (2017): 153–58. http://dx.doi.org/10.20965/jaciii.2017.p0153.

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Sparse multipath channel estimation has recently attracted significant attention due to the sparsity of the channel in broadband wireless communication. Many algorithms have been proposed for sparse multipath channel estimation. Among them, the least mean square (LMS) algorithm, based on adaptive filter, has attracted much attention due to its low complexity and high robustness. However, LMS is usually degraded by the long training signal, which needs large storage space. This paper proposes an improved method that transmits a circulating, short training signal, samples the received signal at
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Bilal, Muhammad, Jawad Ali Shah, Ijaz M. Qureshi, and Kushsairy Kadir. "Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation." International Journal of Biomedical Imaging 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/7803067.

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Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem u
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Haider, Hassaan, Jawad Ali Shah, Kushsairy Kadir, and Najeeb Khan. "Sparse Reconstruction Using Hyperbolic Tangent as Smooth l1-Norm Approximation." Computation 11, no. 1 (2023): 7. http://dx.doi.org/10.3390/computation11010007.

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In the Compressed Sensing (CS) framework, the underdetermined system of linear equation (USLE) can have infinitely many possible solutions. However, we intend to find the sparsest possible solution, which is -norm minimization. However, finding an norm solution out of infinitely many possible solutions is NP-hard problem that becomes non-convex optimization problem. It has been a practically proven fact that norm penalty can be adequately estimated by norm, which recasts a non-convex minimization problem to a convex problem. However, norm non-differentiable and gradient-based minimization algo
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Dissertations / Theses on the topic "Compressive Sensing; Sparsity; L1-Norm"

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Lima, Jose Paulo Rodrigues de. "Representação compressiva de malhas." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-17042014-151933/.

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A compressão de dados é uma área de muito interesse em termos computacionais devido à necessidade de armazená-los e transmiti-los. Em particular, a compressão de malhas possui grande interesse em função do crescimento de sua utilização em jogos tridimensionais e modelagens diversas. Nos últimos anos, uma nova teoria de aquisição e reconstrução de sinais foi desenvolvida, baseada no conceito de esparsidade na minimização da norma L1 e na incoerência do sinal, chamada Compressive Sensing (CS). Essa teoria possui algumas características marcantes, como a aleatoriedade de amostragem e a reconstruç
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Asif, Muhammad Salman. "Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49106.

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This thesis presents compressive sensing algorithms that utilize system dynamics in the sparse signal recovery process. These dynamics may arise due to a time-varying signal, streaming measurements, or an adaptive signal transform. Compressive sensing theory has shown that under certain conditions, a sparse signal can be recovered from a small number of linear, incoherent measurements. The recovery algorithms, however, for the most part are static: they focus on finding the solution for a fixed set of measurements, assuming a fixed (sparse) structure of the signal. In this thesis, we present
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Book chapters on the topic "Compressive Sensing; Sparsity; L1-Norm"

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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.

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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 the same time, where an iterative reweighted l1 norm minimization is used for optimization. After the reconstruction stage, because the optimization is implemented on image patch basis, an additional deblurring or denoising step is used to globally enhance the image quality. Experiment results show that the proposed SR method provides highly efficient results.
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Conference papers on the topic "Compressive Sensing; Sparsity; L1-Norm"

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Zhang, Yang, and Jiong Tang. "Unveiling Structural Integrity Through Data Driven Reconstruction." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-145681.

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Abstract Full-field online sensing provides dense spatial information about vibrating structures in real-time. Such compact sensing is crucial for accurately pinpointing potential damage locations in the structures. To effectively utilize these dense time histories, the sensors must be densely distributed. However, when structures are in operation, long-term deployment of such technologies can be cost-inefficient and often infeasible in real-life scenarios. Additionally, sensor malfunctions in structural health monitoring systems can result in missing measured data, reducing the accuracy of da
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Wang, Sheng, and Nazanin Rahnavard. "Binary Compressive Sensing via Sum of l1-Norm and l(infinity)-Norm Regularization." In MILCOM 2013 - 2013 IEEE Military Communications Conference. IEEE, 2013. http://dx.doi.org/10.1109/milcom.2013.274.

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Krisnanda, Rana, Irma Safitri, and Achmad Rizal. "Huffman Coding Medical Image Watermarking with Compressive Sensing L1 Norm." In 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE). IEEE, 2018. http://dx.doi.org/10.1109/icitisee.2018.8721012.

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Markopoulos, Panos P., and Dimitris G. Chachlakis. "Robust decomposition of 3-way tensors based on L1-norm." In Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, edited by Fauzia Ahmad. SPIE, 2018. http://dx.doi.org/10.1117/12.2307843.

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Liu, Ying, and Dimitris A. Pados. "Conformity evaluation of data samples by L1-norm principal-component analysis." In Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, edited by Fauzia Ahmad. SPIE, 2018. http://dx.doi.org/10.1117/12.2311893.

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Wei, Ruiqi, Qianli Wang, and Zhiqin Zhao. "Two-Dimensional DOA Estimation Based on Separable Observation Model Utilizing Weighted L1-Norm Penalty and Bayesian Compressive Sensing Strategy." In 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2017. http://dx.doi.org/10.1109/icisce.2017.368.

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Wang, Xiao, Feng Xu, and Ya-Qiu Jin. "Numerical simulation of tomography-SAR imaging and the object reconstruction using the compressive sensing approach with L1/2-norm regularization." In 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS). IEEE, 2014. http://dx.doi.org/10.1109/ursigass.2014.6929615.

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