{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:08:39Z","timestamp":1782932919415,"version":"3.54.5"},"reference-count":174,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62275188"],"award-info":[{"award-number":["62275188"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202104041101009"],"award-info":[{"award-number":["202104041101009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202103021223091"],"award-info":[{"award-number":["202103021223091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202103021223047"],"award-info":[{"award-number":["202103021223047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Scientific and Technological Cooperative Project in Shanxi province","award":["62275188"],"award-info":[{"award-number":["62275188"]}]},{"name":"International Scientific and Technological Cooperative Project in Shanxi province","award":["202104041101009"],"award-info":[{"award-number":["202104041101009"]}]},{"name":"International Scientific and Technological Cooperative Project in Shanxi province","award":["202103021223091"],"award-info":[{"award-number":["202103021223091"]}]},{"name":"International Scientific and Technological Cooperative Project in Shanxi province","award":["202103021223047"],"award-info":[{"award-number":["202103021223047"]}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["62275188"],"award-info":[{"award-number":["62275188"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["202104041101009"],"award-info":[{"award-number":["202104041101009"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["202103021223091"],"award-info":[{"award-number":["202103021223091"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["202103021223047"],"award-info":[{"award-number":["202103021223047"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot of pixels in traditional imaging techniques to realize two-dimensional or even multi-dimensional imaging. For SPI using compressed sensing, the target to be imaged is illuminated by a series of patterns with spatial resolution, and then the reflected or transmitted intensity is compressively sampled by the single-pixel detector to reconstruct the target image while breaking the limitation of the Nyquist sampling theorem. Recently, in the area of signal processing using compressed sensing, many measurement matrices as well as reconstruction algorithms have been proposed. It is necessary to explore the application of these methods in SPI. Therefore, this paper reviews the concept of compressive sensing SPI and summarizes the main measurement matrices and reconstruction algorithms in compressive sensing. Further, the performance of their applications in SPI through simulations and experiments is explored in detail, and then their advantages and disadvantages are summarized. Finally, the prospect of compressive sensing with SPI is discussed.<\/jats:p>","DOI":"10.3390\/s23104678","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T01:30:29Z","timestamp":1683855029000},"page":"4678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Comparison of Common Algorithms for Single-Pixel Imaging via Compressed Sensing"],"prefix":"10.3390","volume":"23","author":[{"given":"Wenjing","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Physics and Optoelectronics, Taiyuan University of Technology, No. 79 West Main Street, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2829-881X","authenticated-orcid":false,"given":"Lei","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Physics and Optoelectronics, Taiyuan University of Technology, No. 79 West Main Street, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiping","family":"Zhai","sequence":"additional","affiliation":[{"name":"College of Physics and Optoelectronics, Taiyuan University of Technology, No. 79 West Main Street, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3460-0568","authenticated-orcid":false,"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Physics and Optoelectronics, Taiyuan University of Technology, No. 79 West Main Street, Taiyuan 030024, China"},{"name":"Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, and Shanxi Province, Taiyuan University of Technology, No. 79 West Main Street, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2007.914730","article-title":"Single-pixel imaging via compressive sampling","volume":"25","author":"Duarte","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41566-018-0300-7","article-title":"Principles and prospects for single-pixel imaging","volume":"13","author":"Edgar","year":"2019","journal-title":"Nat. Photonics"},{"key":"ref_3","unstructured":"Sen, P., Chen, B., Garg, G., Marschner, S.R., Horowitz, M., Levoy, M., and Lensch, H.P. (2005). ACM SIGGRAPH 2005 Papers, Association for Computing Machinery."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24752","DOI":"10.1038\/srep24752","article-title":"Multispectral imaging using a single bucket detector","volume":"6","author":"Bian","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10550","DOI":"10.1364\/OE.26.010550","article-title":"Time-resolved multispectral imaging based on an adaptive single-pixel camera","volume":"26","author":"Rousset","year":"2018","journal-title":"Opt. Express"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1364\/OPTICA.5.000315","article-title":"Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements","volume":"5","author":"Zhang","year":"2018","journal-title":"Optica"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"128464","DOI":"10.1016\/j.optcom.2022.128464","article-title":"A super-resolution fusion video imaging spectrometer based on single-pixel camera","volume":"520","author":"Qi","year":"2022","journal-title":"Opt. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11207","DOI":"10.1364\/OE.416388","article-title":"Compressive single-pixel hyperspectral imaging using RGB sensors","volume":"29","author":"Tao","year":"2021","journal-title":"Opt. Express"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"15037","DOI":"10.1364\/OE.455814","article-title":"High speed surface defects detection of mirrors based on ultrafast single-pixel imaging","volume":"30","author":"Liu","year":"2022","journal-title":"Opt. Express"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1038\/s41467-023-36815-3","article-title":"Mid-infrared single-pixel imaging at the single-photon level","volume":"14","author":"Wang","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1038\/nphoton.2014.139","article-title":"Terahertz compressive imaging with metamaterial spatial light modulators","volume":"8","author":"Watts","year":"2014","journal-title":"Nat. Photonics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/TTHZ.2020.2982350","article-title":"Reflective single-pixel terahertz imaging based on compressed sensing","volume":"10","author":"Lu","year":"2020","journal-title":"IEEE Trans. Terahertz Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/s41377-022-00879-5","article-title":"Dual-color terahertz spatial light modulator for single-pixel imaging","volume":"11","author":"Li","year":"2022","journal-title":"Light Sci. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1364\/OE.25.002998","article-title":"Real-time imaging of methane gas leaks using a single-pixel camera","volume":"25","author":"Gibson","year":"2017","journal-title":"Opt. Express"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"E1679","DOI":"10.1073\/pnas.1119511109","article-title":"Compressive fluorescence microscopy for biological and hyperspectral imaging","volume":"109","author":"Studer","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1364\/OPTICA.1.000285","article-title":"Single-pixel infrared and visible microscope","volume":"1","author":"Radwell","year":"2014","journal-title":"Optica"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1364\/AO.442628","article-title":"Fourier photoacoustic microscope improved resolution on single-pixel imaging","volume":"61","author":"Mostafavi","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14424","DOI":"10.1364\/OE.23.014424","article-title":"Compressive imaging in scattering media","volume":"23","author":"Soldevila","year":"2015","journal-title":"Opt. Express"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Deng, H., Wang, G., Li, Q., Sun, Q., Ma, M., and Zhong, X. (2021). Transmissive single-pixel microscopic imaging through scattering media. Sensors, 21.","DOI":"10.3390\/s21082721"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"nwac058","DOI":"10.1093\/nsr\/nwac058","article-title":"Dual-compressed photoacoustic single-pixel imaging","volume":"10","author":"Guo","year":"2023","journal-title":"Natl. Sci. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"231101","DOI":"10.1063\/1.5128621","article-title":"Deep learning optimized single-pixel LiDAR","volume":"115","author":"Radwell","year":"2019","journal-title":"Appl. Phys. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"37484","DOI":"10.1364\/OE.471036","article-title":"Scanning single-pixel imaging lidar","volume":"30","author":"Huang","year":"2022","journal-title":"Opt. Express"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24568","DOI":"10.1364\/OE.396497","article-title":"X-ray imaging of fast dynamics with single-pixel detector","volume":"28","author":"Sefi","year":"2020","journal-title":"Opt. Express"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"056102","DOI":"10.1063\/1.5140322","article-title":"High-resolution sub-sampling incoherent x-ray imaging with a single-pixel detector","volume":"5","author":"He","year":"2020","journal-title":"APL Photonics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15623","DOI":"10.1364\/OE.26.015623","article-title":"Low-cost single-pixel 3D imaging by using an LED array","volume":"26","author":"Chabert","year":"2018","journal-title":"Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1109\/JLT.2022.3211441","article-title":"OAM-basis wavefront single-pixel imaging via compressed sensing","volume":"41","author":"Gao","year":"2023","journal-title":"J. Light. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108140","DOI":"10.1016\/j.optlastec.2022.108140","article-title":"Performance comparison of computational ghost imaging versus single-pixel camera in light disturbance environment","volume":"152","author":"Gong","year":"2022","journal-title":"Opt. Laser Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20160233","DOI":"10.1098\/rsta.2016.0233","article-title":"An introduction to ghost imaging: Quantum and classical","volume":"375","author":"Padgett","year":"2017","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6225","DOI":"10.1038\/ncomms7225","article-title":"Single-pixel imaging by means of Fourier spectrum acquisition","volume":"6","author":"Zhang","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"29838","DOI":"10.1364\/OE.27.029838","article-title":"Single-pixel compressive imaging based on the transformation of discrete orthogonal Krawtchouk moments","volume":"27","author":"Chen","year":"2019","journal-title":"Opt. Express"},{"key":"ref_31","first-page":"311003","article-title":"Hadamard Single-pixel Imaging Using Adaptive Oblique Zigzag Sampling","volume":"50","author":"Su","year":"2021","journal-title":"Acta Photonica Sin."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15463","DOI":"10.1364\/OE.422636","article-title":"DQN based single-pixel imaging","volume":"29","author":"Wang","year":"2021","journal-title":"Opt. Express"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"127326","DOI":"10.1016\/j.optcom.2021.127326","article-title":"Orthogonal single-pixel imaging using an adaptive under-Nyquist sampling method","volume":"500","author":"Xu","year":"2021","journal-title":"Opt. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17460","DOI":"10.1038\/s41598-021-97072-2","article-title":"Compressed sensing in the far-field of the spatial light modulator in high noise conditions","volume":"11","author":"Kallepalli","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1007\/s11265-021-01689-5","article-title":"Efficient spatially-variant single-pixel imaging using block-based compressed sensing","volume":"93","author":"Shin","year":"2021","journal-title":"J. Signal Process. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3464","DOI":"10.1038\/s41598-017-03725-6","article-title":"A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging","volume":"7","author":"Sun","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1364\/OL.41.000886","article-title":"Single-pixel imaging using compressed sensing and wavelength-dependent scattering","volume":"41","author":"Shin","year":"2016","journal-title":"Opt. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information","volume":"52","author":"Romberg","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, L.H., Zhang, W., Guan, M.H., Jiang, S.Y., Fan, M.H., Abu, P.A.R., Chen, C.A., and Chen, S.L. (2019). A Low-Power High-Data-Transmission Multi-Lead ECG Acquisition Sensor System. Sensors, 19.","DOI":"10.3390\/s19224996"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"28190","DOI":"10.1364\/OE.403195","article-title":"Single-pixel imaging 12 years on: A review","volume":"28","author":"Gibson","year":"2020","journal-title":"Optics Express"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1109\/JCN.2013.000083","article-title":"Compressive sensing: From theory to applications, a survey","volume":"15","author":"Qaisar","year":"2013","journal-title":"J. Commun. Netw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4875","DOI":"10.1109\/ACCESS.2018.2793851","article-title":"A systematic review of compressive sensing: Concepts, implementations and applications","volume":"6","author":"Rani","year":"2018","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e3576","DOI":"10.1002\/dac.3576","article-title":"A performance comparison of measurement matrices in compressive sensing","volume":"31","author":"Arjoune","year":"2018","journal-title":"Int. J. Commun. Syst."},{"key":"ref_45","first-page":"197","article-title":"Compressive sensing algorithms for signal processing applications: A survey","volume":"8","author":"Hussein","year":"2015","journal-title":"Int. J. Commun. Netw. Syst. Sci."},{"key":"ref_46","unstructured":"Gunasheela, S.K., and Prasantha, H.S. (2019). Emerging Research in Computing, Information, Communication and Applications, Springer."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/ACCESS.2018.2886471","article-title":"A review of sparse recovery algorithms","volume":"7","author":"Marques","year":"2018","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1364\/JOSAA.35.000078","article-title":"Experimental comparison of single-pixel imaging algorithms","volume":"35","author":"Bian","year":"2018","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106301","DOI":"10.1016\/j.optlaseng.2020.106301","article-title":"Comprehensive comparison of single-pixel imaging methods","volume":"134","author":"Qiu","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Arjoune, Y., Kaabouch, N., El Ghazi, H., and Tamtaoui, A. (2017, January 9\u201311). Compressive sensing: Performance comparison of sparse recovery algorithms. Proceedings of the 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2017.7868430"},{"key":"ref_51","unstructured":"Cand\u00e8s, E.J. (2006, January 22\u201330). Compressive sampling. Proceedings of the International Congress of Mathematicians, Madrid, Spain."},{"key":"ref_52","unstructured":"Baraniuk, R., Davenport, M.A., Duarte, M.F., and Hegde, C. (2011). An introduction to compressive sensing. Connex. e-Textb."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1090\/S0894-0347-08-00600-0","article-title":"Counting faces of randomly projected polytopes when the projection radically lowers dimension","volume":"22","author":"Donoho","year":"2009","journal-title":"J. Am. Math. Soc."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Mallat, S. (1999). A Wavelet Tour of Signal Processing, Elsevier.","DOI":"10.1016\/B978-012466606-1\/50008-8"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/MSP.2007.4286567","article-title":"Life beyond bases: The advent of frames (Part I)","volume":"24","author":"Kovacevic","year":"2007","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/MSP.2007.904809","article-title":"Life beyond bases: The advent of frames (Part II)","volume":"24","author":"Kovacevic","year":"2007","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/JPROC.2010.2040551","article-title":"Dictionaries for sparse representation modeling","volume":"98","author":"Rubinstein","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3320","DOI":"10.1109\/TIT.2003.820031","article-title":"Sparse representations in unions of bases","volume":"49","author":"Gribonval","year":"2003","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.crma.2008.03.014","article-title":"The restricted isometry property and its implications for compressed sensing","volume":"346","author":"Candes","year":"2008","journal-title":"Comptes Rendus Math."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4789","DOI":"10.1109\/TIT.2008.929958","article-title":"Fast Solution of \u21130-Norm Minimization Problems When the Solution May Be Sparse","volume":"54","author":"Donoho","year":"2008","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1088\/0266-5611\/23\/3\/008","article-title":"Sparsity and incoherence in compressive sampling","volume":"23","author":"Candes","year":"2007","journal-title":"Inverse Probl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1137\/040616413","article-title":"Condition numbers of Gaussian random matrices","volume":"27","author":"Chen","year":"2005","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhang, G., Jiao, S., Xu, X., and Wang, L. (2010, January 20\u201323). Compressed sensing and reconstruction with bernoulli matrices. Proceedings of the The 2010 IEEE International Conference on Information and Automation, Harbin, China.","DOI":"10.1109\/ICINFA.2010.5512379"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4053","DOI":"10.1109\/TSP.2011.2161982","article-title":"Structured compressed sensing: From theory to applications","volume":"59","author":"Duarte","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.sigpro.2005.05.029","article-title":"Extensions of compressed sensing","volume":"86","author":"Tsaig","year":"2006","journal-title":"Signal Process."},{"key":"ref_66","first-page":"194","article-title":"Compressed sensing and reconstruction with semi-hadamard matrices","volume":"Volume 1","author":"Zhang","year":"2010","journal-title":"Proceedings of the 2010 2nd International Conference on Signal Processing Systems"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Yin, W., Morgan, S., Yang, J., and Zhang, Y. (2010, January 14). Practical compressive sensing with Toeplitz and circulant matrices. Proceedings of the Visual Communications and Image Processing, Huangshan, China.","DOI":"10.1117\/12.863527"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Do, T.T., Tran, T.D., and Gan, L. (2008). Fast compressive sampling with structurally random matrices. 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA, 30 March\u20134 April 2008, IEEE.","DOI":"10.1109\/ICASSP.2008.4518373"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TSP.2011.2170977","article-title":"Fast and efficient compressive sensing using structurally random matrices","volume":"60","author":"Do","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_70","unstructured":"Sarvotham, S., Baron, D., and Baraniuk, R.G. (2006). Compressed sensing reconstruction via belief propagation. Preprint, 14."},{"key":"ref_71","unstructured":"Ak\u00e7akaya, M., Park, J., and Tarokh, V. (2009). Compressive sensing using low density frames. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/JPROC.2010.2045092","article-title":"Sparse recovery using sparse matrices","volume":"98","author":"Gilbert","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TSP.2009.2027773","article-title":"Bayesian compressive sensing via belief propagation","volume":"58","author":"Baron","year":"2009","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5369","DOI":"10.1109\/TSP.2011.2163402","article-title":"A coding theory approach to noisy compressive sensing using low density frames","volume":"59","author":"Park","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Baron, D., Duarte, M.F., Wakin, M.B., Sarvotham, S., and Baraniuk, R.G. (2009). Distributed compressive sensing. arXiv.","DOI":"10.21236\/ADA521228"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5859","DOI":"10.1109\/TSP.2011.2166546","article-title":"Concentration of measure for block diagonal matrices with applications to compressive signal processing","volume":"59","author":"Park","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"5035","DOI":"10.1109\/TIT.2012.2196256","article-title":"Deterministic construction of compressed sensing matrices via algebraic curves","volume":"58","author":"Li","year":"2012","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Berinde, R., Gilbert, A.C., Indyk, P., Karloff, H., and Strauss, M.J. (2008, January 23\u201326). Combining geometry and combinatorics: A unified approach to sparse signal recovery. Proceedings of the 2008 46th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA.","DOI":"10.1109\/ALLERTON.2008.4797639"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/JSTSP.2010.2043161","article-title":"Construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property","volume":"4","author":"Calderbank","year":"2010","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.jco.2007.04.002","article-title":"Deterministic constructions of compressed sensing matrices","volume":"23","author":"DeVore","year":"2007","journal-title":"J. Complex."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"192795","DOI":"10.1155\/2013\/192795","article-title":"Deterministic sensing matrices in compressive sensing: A survey","volume":"2013","author":"Nguyen","year":"2013","journal-title":"Sci. World J."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TSP.2011.2169249","article-title":"Matrices with small coherence using p-ary block codes","volume":"60","author":"Amini","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TSP.2010.2082536","article-title":"Sparse recovery of nonnegative signals with minimal expansion","volume":"59","author":"Khajehnejad","year":"2010","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/TSP.2007.900760","article-title":"Optimized projections for compressed sensing","volume":"55","author":"Elad","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Nhat, V.D.M., Vo, D., Challa, S., and Lee, S. (2008, January 14\u201316). Efficient projection for compressed sensing. Proceedings of the Seventh IEEE\/ACIS International Conference on Computer and Information Science (icis 2008), Portland, OR, USA.","DOI":"10.1109\/ICIS.2008.72"},{"key":"ref_86","unstructured":"Wu, S., Dimakis, A., Sanghavi, S., Yu, F., Holtmann-Rice, D., Storcheus, D., Rostamizadeh, A., and Kumar, S. (2019). International Conference on Machine Learning, PMLR."},{"key":"ref_87","unstructured":"Wu, Y., Rosca, M., and Lillicrap, T. (2019). International Conference on Machine Learning, PMLR."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1002\/ima.22651","article-title":"Deep learning on compressed sensing measurements in pneumonia detection","volume":"32","author":"Islam","year":"2022","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"39077","DOI":"10.1007\/s11042-022-12894-0","article-title":"Genetic algorithm based framework for optimized sensing matrix design in compressed sensing","volume":"81","author":"Ahmed","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_90","unstructured":"Pope, G. (2009). Compressive Sensing: A Summary of Reconstruction Algorithms. [Master\u2019s Thesis, ETH, Swiss Federal Institute of Technology Zurich, Department of Computer Science]."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Siddamal, K.V., Bhat, S.P., and Saroja, V.S. (2015, January 26\u201327). A survey on compressive sensing. Proceedings of the 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India.","DOI":"10.1109\/ECS.2015.7124986"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Carmi, A.Y., Mihaylova, L., and Godsill, S.J. (2014). Compressed Sensing & Sparse Filtering, Springer.","DOI":"10.1007\/978-3-642-38398-4"},{"key":"ref_93","unstructured":"Hameed, M.A. (2012). Comparative Analysis of Orthogonal Matching Pursuit and Least Angle Regression, Michigan State University, Electrical Engineering."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1137\/0907087","article-title":"Linear inversion of band-limited reflection seismograms","volume":"7","author":"Santosa","year":"1986","journal-title":"SIAM J. Sci. Stat. Comput."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1137\/0149053","article-title":"Uncertainty principles and signal recovery","volume":"49","author":"Donoho","year":"1989","journal-title":"SIAM J. Appl. Math."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1137\/0152031","article-title":"Signal recovery and the large sieve","volume":"52","author":"Donoho","year":"1992","journal-title":"SIAM J. Appl. Math."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1073\/pnas.0437847100","article-title":"Optimally sparse representation in general (nonorthogonal) dictionaries via \u21131 minimization","volume":"100","author":"Donoho","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1109\/TIT.2002.801410","article-title":"A generalized uncertainty principle and sparse representation in pairs of bases","volume":"48","author":"Elad","year":"2002","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s40305-013-0010-2","article-title":"Theory of compressive sensing via \u21131-minimization: A non-rip analysis and extensions","volume":"1","author":"Zhang","year":"2013","journal-title":"J. Oper. Res. Soc. China"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s00041-008-9045-x","article-title":"Enhancing sparsity by reweighted \u21131 minimization","volume":"14","author":"Candes","year":"2008","journal-title":"J. Fourier Anal. Appl."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1137\/S003614450037906X","article-title":"Atomic decomposition by basis pursuit","volume":"43","author":"Chen","year":"2001","journal-title":"SIAM Rev."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"3760","DOI":"10.1109\/TSP.2007.894287","article-title":"Greedy basis pursuit","volume":"55","author":"Huggins","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Biegler, L.T. (2010). Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes, Society for Industrial and Applied Mathematics.","DOI":"10.1137\/1.9780898719383"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/10618600.1998.10474784","article-title":"Penalized regressions: The bridge versus the lasso","volume":"7","author":"Fu","year":"1998","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1109\/TIT.2013.2252232","article-title":"Asymptotic analysis of complex LASSO via complex approximate message passing (CAMP)","volume":"59","author":"Maleki","year":"2013","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"4203","DOI":"10.1109\/TIT.2005.858979","article-title":"Decoding by linear programming","volume":"51","author":"Candes","year":"2005","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_108","unstructured":"Candes, E., and Romberg, J. (2005, April 14). l1-Magic: Recovery of Sparse Signals Via Convex Programming. Available online: www.acm.caltech.edu\/l1magic\/downloads\/l1magic.pdf."},{"key":"ref_109","first-page":"2313","article-title":"The Dantzig selector: Statistical estimation when p is much larger than n","volume":"35","author":"Candes","year":"2007","journal-title":"Ann. Stat."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5815\/ijigsp.2015.10.01","article-title":"A survey of compressive sensing based greedy pursuit reconstruction algorithms","volume":"7","author":"Meenakshi","year":"2015","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"ref_111","first-page":"126","article-title":"A survey on greedy reconstruction algorithms in compressive sensing","volume":"5","author":"Akhila","year":"2016","journal-title":"Int. J. Res. Comput. Commun. Technol."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1109\/TIT.2004.834793","article-title":"Greed is good: Algorithmic results for sparse approximation","volume":"50","author":"Tropp","year":"2004","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/78.258082","article-title":"Matching pursuits with time-frequency dictionaries","volume":"41","author":"Mallat","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_114","unstructured":"Pati, Y.C., Rezaiifar, R., and Krishnaprasad, P.S. (1993, January 1\u20133). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/BF02124742","article-title":"Some remarks on greedy algorithms","volume":"5","author":"DeVore","year":"1996","journal-title":"Adv. Comput. Math."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1109\/TSP.2016.2634550","article-title":"A sharp condition for exact support recovery with orthogonal matching pursuit","volume":"65","author":"Wen","year":"2016","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Wang, J. (2015). Support recovery with orthogonal matching pursuit in the presence of noise: A new analysis. arXiv.","DOI":"10.1109\/TSP.2015.2468676"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s10208-008-9031-3","article-title":"Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit","volume":"9","author":"Needell","year":"2009","journal-title":"Found. Comput. Math."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/JSTSP.2010.2042412","article-title":"Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit","volume":"4","author":"Needell","year":"2010","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.acha.2008.07.002","article-title":"CoSaMP: Iterative signal recovery from incomplete and inaccurate samples","volume":"26","author":"Needell","year":"2009","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1109\/TIT.2009.2016006","article-title":"Subspace pursuit for compressive sensing signal reconstruction","volume":"55","author":"Dai","year":"2009","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s00041-008-9035-z","article-title":"Iterative thresholding for sparse approximations","volume":"14","author":"Blumensath","year":"2008","journal-title":"J. Fourier Anal. Appl."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.acha.2009.04.002","article-title":"Iterative hard thresholding for compressed sensing","volume":"27","author":"Blumensath","year":"2009","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/JSTSP.2010.2042411","article-title":"Normalized iterative hard thresholding: Guaranteed stability and performance","volume":"4","author":"Blumensath","year":"2010","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/LSP.2007.898300","article-title":"Exact reconstruction of sparse signals via nonconvex minimization","volume":"14","author":"Chartrand","year":"2007","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.acha.2014.06.003","article-title":"Stable recovery of sparse signals via lp-minimization","volume":"38","author":"Wen","year":"2015","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Kanevsky, D., Carmi, A., Horesh, L., Gurfil, P., Ramabhadran, B., and Sainath, T.N. (2010, January 26\u201329). Kalman filtering for compressed sensing. Proceedings of the 2010 13th International Conference on Information Fusion, Edinburgh, UK.","DOI":"10.1109\/ICIF.2010.5711877"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"035020","DOI":"10.1088\/0266-5611\/24\/3\/035020","article-title":"Restricted isometry properties and nonconvex compressive sensing","volume":"24","author":"Chartrand","year":"2008","journal-title":"Inverse Probl."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Chartrand, R., and Yin, W. (\u20134, January 30). Iteratively reweighted algorithms for compressive sensing. Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NA, USA.","DOI":"10.1109\/ICASSP.2008.4518498"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TSP.2004.831016","article-title":"Sparse Bayesian learning for basis selection","volume":"52","author":"Wipf","year":"2004","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1109\/TSP.2007.914345","article-title":"Bayesian compressive sensing","volume":"56","author":"Ji","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Ji, S., and Carin, L. (2007, January 20\u201324). Bayesian compressive sensing and projection optimization. Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, USA.","DOI":"10.1145\/1273496.1273544"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Bernardo, J.M., and Smith, A.F.M. (1994). Bayesian Theory, Wiley.","DOI":"10.1002\/9780470316870"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1137\/070703983","article-title":"Bregman iterative algorithms for \u21131-minimization with applications to compressed sensing","volume":"1","author":"Yin","year":"2008","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1137\/040605412","article-title":"An iterative regularization method for total variation-based image restoration","volume":"4","author":"Osher","year":"2005","journal-title":"Multiscale Model. Simul."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1090\/S0025-5718-08-02189-3","article-title":"Linearized Bregman iterations for compressed sensing","volume":"78","author":"Cai","year":"2009","journal-title":"Math. Comput."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1137\/080725891","article-title":"The split Bregman method for \u21131-regularized problems","volume":"2","author":"Goldstein","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/B:JMIV.0000011321.19549.88","article-title":"An algorithm for total variation minimization and applications","volume":"20","author":"Chambolle","year":"2004","journal-title":"J. Math. Imaging Vis."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"2842","DOI":"10.1137\/080732894","article-title":"An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise","volume":"31","author":"Yang","year":"2009","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/83.392335","article-title":"Nonlinear image recovery with half-quadratic regularization","volume":"4","author":"Geman","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_142","unstructured":"Li, C. (2010). An Efficient Algorithm for Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing, Rice University."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"6202","DOI":"10.1109\/TSP.2012.2218810","article-title":"Generalized orthogonal matching pursuit","volume":"60","author":"Wang","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Rangan, S. (August, January 31). Generalized approximate message passing for estimation with random linear mixing. Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, St. Petersburg, Russia.","DOI":"10.1109\/ISIT.2011.6033942"},{"key":"ref_145","unstructured":"Khajehnejad, M.A., Xu, W., Avestimehr, A.S., and Hassibi, B. (July, January 28). Weighted \u21131 minimization for sparse recovery with prior information. Proceedings of the 2009 IEEE International Symposium on Information Theory, Seoul, Republic of Korea."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"De Paiva, N.M., Marques, E.C., and de Barros Naviner, L.A. (2017, January 6\u20138). Sparsity analysis using a mixed approach with greedy and LS algorithms on channel estimation. Proceedings of the 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP), Paris, France.","DOI":"10.1109\/ICFSP.2017.8097148"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/TIT.2014.2310482","article-title":"Multipath matching pursuit","volume":"60","author":"Kwon","year":"2014","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/LCOMM.2016.2642922","article-title":"A novel sufficient condition for generalized orthogonal matching pursuit","volume":"21","author":"Wen","year":"2016","journal-title":"IEEE Commun. Lett."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Sun, H., and Ni, L. (2013, January 12\u201313). Compressed sensing data reconstruction using adaptive generalized orthogonal matching pursuit algorithm. Proceedings of the 2013 3rd International Conference on Computer Science and Network Technology, Dalian, China.","DOI":"10.1109\/ICCSNT.2013.6967295"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/LSP.2011.2147313","article-title":"Backtracking-based matching pursuit method for sparse signal reconstruction","volume":"18","author":"Huang","year":"2011","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_151","unstructured":"Gilbert, A.C., Strauss, M.J., Tropp, J.A., and Vershynin, R. (2006). Algorithmic linear dimension reduction in the l_1 norm for sparse vectors. arXiv."},{"key":"ref_152","first-page":"289","article-title":"CGIHT: Conjugate gradient iterative hard thresholding for compressed sensing and matrix completion","volume":"4","author":"Blanchard","year":"2015","journal-title":"Inf. Inference A J. IMA"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Zhu, X., Dai, L., Dai, W., Wang, Z., and Moonen, M. (2015, January 26\u201328). Tracking a dynamic sparse channel via differential orthogonal matching pursuit. Proceedings of the MILCOM 2015\u20132015 IEEE Military Communications Conference, Tampa, FL, USA.","DOI":"10.1109\/MILCOM.2015.7357541"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1016\/j.dsp.2013.05.007","article-title":"Compressed sensing signal recovery via forward\u2013backward pursuit","volume":"23","author":"Karahanoglu","year":"2013","journal-title":"Digit. Signal Process."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"59141A","DOI":"10.1117\/12.615931","article-title":"Improved time bounds for near-optimal sparse Fourier representations","volume":"Volume 5914","author":"Gilbert","year":"2005","journal-title":"Wavelets XI"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1137\/100806278","article-title":"Hard thresholding pursuit: An algorithm for compressive sensing","volume":"49","author":"Foucart","year":"2011","journal-title":"SIAM J. Numer. Anal."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Gilbert, A.C., Strauss, M.J., Tropp, J.A., and Vershynin, R. (2007, January 11\u201313). One sketch for all: Fast algorithms for compressed sensing. Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, San Diego, CA, USA.","DOI":"10.1145\/1250790.1250824"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"S104","DOI":"10.1137\/120876459","article-title":"Normalized iterative hard thresholding for matrix completion","volume":"35","author":"Tanner","year":"2013","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1109\/TSP.2010.2044841","article-title":"An adaptive greedy algorithm with application to nonlinear communications","volume":"58","author":"Mileounis","year":"2010","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/JCN.2016.000100","article-title":"Sparse signal recovery via tree search matching pursuit","volume":"18","author":"Lee","year":"2016","journal-title":"J. Commun. Netw."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"6664","DOI":"10.1109\/TIT.2019.2916359","article-title":"Vector approximate message passing","volume":"65","author":"Rangan","year":"2019","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/cpa.20042","article-title":"An iterative thresholding algorithm for linear inverse problems with a sparsity constraint","volume":"57","author":"Daubechies","year":"2004","journal-title":"Commun. Pure Appl. Math. J. Issued Courant Inst. Math. Sci."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/080716542","article-title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","volume":"2","author":"Beck","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"18914","DOI":"10.1073\/pnas.0909892106","article-title":"Message-passing algorithms for compressed sensing","volume":"106","author":"Donoho","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Montanari, A., Eldar, Y.C., and Kutyniok, G. (2012). Graphical models concepts in compressed sensing. Compress. Sens., 394\u2013438.","DOI":"10.1017\/CBO9780511794308.010"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann. Stat."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/JSTSP.2007.910281","article-title":"Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems","volume":"1","author":"Figueiredo","year":"2007","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/78.558475","article-title":"Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm","volume":"45","author":"Gorodnitsky","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Do, T.T., Gan, L., Nguyen, N., and Tran, T.D. (2008, January 26\u201329). Sparsity adaptive matching pursuit algorithm for practical compressed sensing. Proceedings of the 2008 42nd Asilomar Conference on Signals, SYSTEMS and computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2008.5074472"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1109\/TSP.2007.916124","article-title":"Gradient pursuits","volume":"56","author":"Blumensath","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"e2201062119","DOI":"10.1073\/pnas.2201062119","article-title":"Revisiting \u21131-wavelet compressed-sensing MRI in the era of deep learning","volume":"119","author":"Gu","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Adler, A., Boublil, D., Elad, M., and Zibulevsky, M. (2016). A deep learning approach to block-based compressed sensing of images. arXiv.","DOI":"10.1109\/MMSP.2017.8122281"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Xie, Y., and Li, Q. (2022). A review of deep learning methods for compressed sensing image reconstruction and its medical applications. Electronics, 11.","DOI":"10.3390\/electronics11040586"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Zonzini, F., Carbone, A., Romano, F., Zauli, M., and De Marchi, L. (2022). Machine learning meets compressed sensing in vibration-based monitoring. Sensors, 22.","DOI":"10.3390\/s22062229"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:33:18Z","timestamp":1760124798000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,11]]},"references-count":174,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104678"],"URL":"https:\/\/doi.org\/10.3390\/s23104678","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,11]]}}}