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Stabilization Techniques for Iterative Algorithms in Compressed Sensing [article]

Carmen Sippel, Robert F. H. Fischer
2021 arXiv   pre-print
Algorithms for signal recovery in compressed sensing (CS) are often improved by stabilization techniques, such as damping, or the less widely known so-called fractional approach, which is based on the  ...  The effects of the stabilization procedures are examined and compared via numerical simulations and we show that a combination of several procedures can be beneficial for the performance of the algorithm  ...  CONCLUSION This work considered stabilization techniques for iterative algorithms in compressed sensing. The procedures of damping and fractional updates were examined and interpreted in detail.  ... 
arXiv:2109.14917v1 fatcat:2dip5pi3vfcrvp4baj2zapclt4

Reconstruction of Compressively Sensed Images using Convex Tikhonov Sparse Dictionary Learning and Adaptive Spectral Filtering [article]

Nishant Deepak Keni, Amol Mangirish Singbal, Rizwan Ahmed
2019 arXiv   pre-print
Consequently, high PSNR and low MSE is obtained using the proposed algorithm for our compressive sensing framework.  ...  In this article, we use a closed form stabilized convex optimization technique for both sparse coding and dictionary learning.  ...  (Structural Similarity) after 1 iteration of our proposed algorithmic framework for cases where recovery is done on 2%, 6% and 16% compressive sensing on the sample image ( Fig. 3) with different sensing  ... 
arXiv:1801.09135v2 fatcat:cufxwsrievcztif26kwoyksv4e

Compressive Sensing–Based Interior Tomography

Hengyong Yu, Ge Wang, Jiang Hsieh, Daniel W. Entrikin, Sandra Ellis, Baodong Liu, John Jeffrey Carr
2011 Journal of computer assisted tomography  
Compressive sensing (CS)-based interior tomography is a state-of-the-art method for accurate image reconstruction from only locally truncated projections.  ...  Inspired by the recently developed compressive sensing (CS) theory, in 2009 we proved that exact interior reconstruction is achievable with an interior scan if an ROI is piecewise constant or piecewise  ...  Compressive sensing-based interior reconstruction is a promising direction for accurate reconstruction from truncated projections.  ... 
doi:10.1097/rct.0b013e318231c578 pmid:22082550 pmcid:PMC3246307 fatcat:l6bmsc3ckzfmvm3a3vpxnpwmtq

Iterative Methods For Random Sampling Recovery And Compressed Sensing Recovery

Masomeh Azghani, Farokh Marvasti
2013 Zenodo  
Publication in the conference proceedings of SampTA, Bremen, Germany, 2013  ...  ITERATIVE METHOD FOR COMPRESSED SENSING RECOVERY (IMATCS) In this section, the proposed Iterative Method for Compressed Sensing recovery (IMATCS) is illustrated.  ...  IHT is proposed for compressed sensing recovery of sparse signals when the sparsity number of the signal is known. In [8] , normalized IHT algorithm is proposed which is a stabilized version of IHT.  ... 
doi:10.5281/zenodo.54373 fatcat:uwmtvx4dq5anrfjrxjuvbe55ji

Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

Ishani Mishra, Sanjay Jain
2022 Intelligent Automation and Soft Computing  
For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN.  ...  In this paper, we provide an efficient compressive sensing framework which strives to improve the reconstruction process, by adjusting the sensing matrix during the compression phase using the rain optimization  ...  Acknowledgement: The authors with a deep sense of gratitude would thank the supervisor for his guidance and constant support rendered during this research.  ... 
doi:10.32604/iasc.2022.022860 fatcat:nopfxdfz35c6jdtd53ah2jdyvq

Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems [article]

Bryan Liu, Toshiaki Koike-Akino, Ye Wang, Kieran Parsons
2022 arXiv   pre-print
Numerical results show that the VQC method can outperform modern compressed sensing techniques using an element-wise denoiser.  ...  This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem.  ...  Compressed Sensing: OAMP Algorithm As a state-of-the-art CS technique, we briefly describe the OAMP algorithm [4] .  ... 
arXiv:2205.08603v1 fatcat:hzdobif6evhu5ovzuc2yoz6kp4

A sparsity adaptive compressed signal reconstruction based on sensing dictionary

Shen Zhiyuan, Wang Qianqian, Cheng Xinmiao
2021 Journal of Systems Engineering and Electronics  
Signal reconstruction is a significantly important theoretical issue for compressed sensing.  ...  Using the adaptive sparsity method, an iterative signal reconstruction algorithm is proposed. The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.  ...  Introduction Compressed sensing (CS), which has been studied by many scholars [1, 2] , is a vital technique for decreasing the number of samples during information acquisition.  ... 
doi:10.23919/jsee.2021.000114 fatcat:utk3vlxthvfwpjweswcj55drpq

Sigma delta quantization for compressive sensing

Petros Boufounos, Richard G. Baraniuk, Dimitri Van De Ville, Vivek K. Goyal, Manos Papadakis
2007 Wavelets XII  
Compressive sensing is a new data acquisition technique that aims to measure sparse and compressible signals at close to their intrinsic information rate rather than their Nyquist rate.  ...  Recent results in compressive sensing show that a sparse or compressible signal can be reconstructed from very few measurements with an incoherent, and even randomly generated, dictionary.  ...  INTRODUCTION Compressive sensing is a new low-rate signal acquisition method for signals that are sparse or compressible.  ... 
doi:10.1117/12.734880 fatcat:wf6pfbghgvg6bjnymsc2yy7nie

Image reconstruction algorithm based on compressed sensing for electrical capacitance tomography

Lifeng Zhang, Zhaolin Liu, Pei Tian, Charles M. Falco, Xudong Jiang
2016 Eighth International Conference on Digital Image Processing (ICDIP 2016)  
capacitance tomography system, a Novel image reconstruction algorithm based on compressed sensing is proposed in the paper.  ...  This new algorithm presents a feasible and effective way to research on image reconstruction algorithm for Electrical Capacitance Tomography System.  ...  Image Reconstruction Algorithm based on Compressed Sensing Compressed Sensing Principle According to compressive sensing principle, if the signal is compressible or sparse in a transform domain, the  ... 
doi:10.1117/12.2244311 fatcat:tkkknjv3yrdjhfmuz7kbkyfxwq

An Energy-Efficient Data Acquisition Technique for Hierarchical Cluster-Based Wireless Sensor Networks

Ahmed M. Khedr, P. V. Pravija Raj, Amal Al Ali
2020 Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications  
The features of CS theory such as signal compression, robustness, computational asymmetry, and stability make it a good choice for WSNs operating in resource constrained environment.  ...  The Compressive Sensing (CS) technique renders a new sampling strategy to reduce the size of data being transmitted and therefore minimize the energy utilization in WSNs [22, 8, 23, 24] .  ...  In [38] , an energy consumption configuration model joint distribution compressive sensing and quantization compressive sensing is proposed for energy efficient data gathering.  ... 
doi:10.22667/jowua.2020.09.30.070 dblp:journals/jowua/KhedrRA20 fatcat:iyj3iqg75zdgzmvw5xgvpw5o5u

Electrical Capacitance Tomography: A compressive sensing approach

Hongcheng Wang, Igor Fedchenia, Sergey Shishkin, Alan Finn, Lance Smith, Meredith Colket
2012 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings  
Here, Compressive Sensing is used to provide better reconstruction from the small number of measurements.  ...  In this paper, we present Total Variation (TV) regularization for ECT image reconstruction, and apply an efficient Split-Bregman Iteration (SBI) approach to solve the problem.  ...  ALGORITHM In this section, we describe the compressive sensing approach to solve the ill-posed problem.  ... 
doi:10.1109/ist.2012.6295574 fatcat:4coplppm45eqxpxarqm2dfc3b4

Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing

Wael Guibène, Dirk Slock
2013 Journal of Sensors  
Simulation results for realistic network topologies and different compressed sensing reconstruction algorithms testify to the performance and the feasibility of the proposed technique to enable in a single  ...  We propose to fuse two main enabling features in cognitive radio systems (CRS): spectrum sensing and location awareness in a single compressed sensing based formalism.  ...  These two algorithms provide a similar guarantee of stability as BP with the same iterative property of fast convergence as greedy algorithms.  ... 
doi:10.1155/2013/606413 fatcat:vmqgeooqnvaoxbn5hrxjlvstjy

Low-Complexity Compressive Spectrum Sensing for Large-Scale Real-Time Processing

Xingjian Zhang, Yuan Ma, Haoran Qi, Yue Gao
2018 IEEE Wireless Communications Letters  
Index Terms-Compressed sensing, cognitive radio, iterative algorithms.  ...  Therefore, we propose a low-complexity compressive spectrum sensing algorithm that is suitable for large-scale real-time processing problem.  ...  In this letter, a low-complexity compressive spectrum sensing algorithm is proposed for large-scale realtime processing.  ... 
doi:10.1109/lwc.2018.2810231 fatcat:h4dfs3semvev3mmpssagafu24m

Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks

Fanzi Zeng, Chen Li, Zhi Tian
2011 IEEE Journal on Selected Topics in Signal Processing  
These decentralized techniques are developed for both cases of with and without channel knowledge.  ...  algorithm is derived to attain high sensing performance at a reasonable computational cost and power overhead.  ...  procedure for decentralized collaborative compressed sensing in the presence of sparse spectral innovations, as tabulated in Algorithm 2.  ... 
doi:10.1109/jstsp.2010.2055037 fatcat:dq7j5fgkcna3nnj2tbn2za7yiu

Iterative algorithms for compressed sensing with partially known support

Rafael E. Carrillo, Luisa F. Polania, Kenneth E. Barner
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS.  ...  In this paper, we extend the ideas of these works to modify three iterative algorithms to incorporate the known support in the recovery process.  ...  This technique is extended in [8] to the case of corrupted measurements and compressible signals and a stability result is proven for this general case.  ... 
doi:10.1109/icassp.2010.5495901 dblp:conf/icassp/CarrilloPB10 fatcat:rvtl5pi2ezcbdidvl36b5cfgba
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