{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:05:58Z","timestamp":1781157958475,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.<\/jats:p>","DOI":"10.3390\/s21196551","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"6551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Coded Aperture Hyperspectral Image Reconstruction"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0509-6799","authenticated-orcid":false,"given":"Ignacio","family":"Garc\u00eda-S\u00e1nchez","sequence":"first","affiliation":[{"name":"Department of Computer Engineering & CITIC Research Center, University of A Coru\u00f1a, Campus de Elvi\u00f1a s\/n, 15071 A Coru\u00f1a, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2905-9052","authenticated-orcid":false,"given":"\u00d3scar","family":"Fresnedo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering & CITIC Research Center, University of A Coru\u00f1a, Campus de Elvi\u00f1a s\/n, 15071 A Coru\u00f1a, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7554-1758","authenticated-orcid":false,"given":"Jos\u00e9 P.","family":"Gonz\u00e1lez-Coma","sequence":"additional","affiliation":[{"name":"Defense University Center, The Spanish Naval Academy, University of Vigo, Plaza de Espa\u00f1a 2, Mar\u00edn, 36920 Pontevedra, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3801-012X","authenticated-orcid":false,"given":"Luis","family":"Castedo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering & CITIC Research Center, University of A Coru\u00f1a, Campus de Elvi\u00f1a s\/n, 15071 A Coru\u00f1a, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/B978-0-444-63977-6.00021-3","article-title":"Hyperspectral imaging in medical applications","volume":"Volume 32","author":"Fei","year":"2020","journal-title":"Data Handling in Science and Technology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1364\/BOE.381257","article-title":"Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning","volume":"11","author":"Halicek","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TCI.2020.3022825","article-title":"Hyperspectral and multispectral image fusion under spectrally varying spatial blurs\u2013Application to high dimensional infrared astronomical imaging","volume":"6","author":"Guilloteau","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/B978-0-444-63977-6.00018-3","article-title":"Hyperspectral imaging in crop fields: Precision agriculture","volume":"Volume 32","author":"Caballero","year":"2020","journal-title":"Data Handling in Science and Technology"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gonzalez, S.A.R., Shimoni, M., Plaza, J., Plaza, A., Renhorn, I., and Ahlberg, J. (2020, January 22\u201326). The Detection of Concealed Targets in Woodland Areas using Hyperspectral Imagery. Proceedings of the 2020 IEEE Latin American GRSS ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile.","DOI":"10.1109\/LAGIRS48042.2020.9165611"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/MSP.2013.2279507","article-title":"Sparsity and Structure in Hyperspectral Imaging: Sensing, Reconstruction, and Target Detection","volume":"31","author":"Willett","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103115","DOI":"10.1016\/j.infrared.2019.103115","article-title":"Status and application of advanced airborne hyperspectral imaging technology: A review","volume":"104","author":"Jia","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6515","DOI":"10.1109\/JSTARS.2021.3088945","article-title":"An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors","volume":"14","author":"Jurado","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.OE.52.9.090901","article-title":"Review of snapshot spectral imaging technologies","volume":"52","author":"Hagen","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_11","unstructured":"West, M., Grossman, J., and Galvan, C. (2018). Commercial Snapshot Spectral Imaging: The Art of the Possible, The MITRE Corporation. Technical Report."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cheng, N., Huang, H., Zhang, L., and Wang, L. (2021, January 10\u201315). Snapshot Hyperspectral Imaging Based on Weighted High-order Singular Value Regularization. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412003"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"B44","DOI":"10.1364\/AO.47.000B44","article-title":"Single disperser design for coded aperture snapshot spectral imaging","volume":"47","author":"Wagadarikar","year":"2008","journal-title":"Appl. Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/MSP.2013.2278763","article-title":"Compressive Coded Aperture Spectral Imaging: An Introduction","volume":"31","author":"Arce","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1364\/AO.414402","article-title":"Enhancement of CASSI by a zero-order image employing a single detector","volume":"60","year":"2021","journal-title":"Appl. Opt."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1109\/18.959265","article-title":"Uncertainty principles and ideal atomic decomposition","volume":"47","author":"Donoho","year":"2001","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5406","DOI":"10.1109\/TIT.2006.885507","article-title":"Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?","volume":"52","author":"Candes","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Atta, R.E., Kasem, H.M., and Attia, M. (2019, January 12\u201314). A comparison study for image compression based on compressive sensing. Proceedings of the Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), Hangzhou, China.","DOI":"10.1117\/12.2557296"},{"key":"ref_20","unstructured":"Mousavi, A., Rezaee, M., and Ayanzadeh, R. (2019). A survey on compressive sensing: Classical results and recent advancements. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Manchanda, R., and Sharma, K. (2020, January 21\u201322). A Review of Reconstruction Algorithms in Compressive Sensing. Proceedings of the 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM), Dehradun, India.","DOI":"10.1109\/ICACCM50413.2020.9212838"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chatterjee, A., and Yuen, P.W. (October, January 26). Rapid Estimation of Orthogonal Matching Pursuit Representation. Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323532"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1109\/JPROC.2010.2044010","article-title":"Computational Methods for Sparse Solution of Linear Inverse Problems","volume":"98","author":"Tropp","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_24","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_25","unstructured":"Herrity, K.K., Gilbert, A.C., and Tropp, J.A. (2006, January 14\u201319). Sparse Approximation Via Iterative Thresholding. Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2992","DOI":"10.1109\/TIP.2007.909319","article-title":"A New TwIST: Two-Step Iterative Shrinkage\/Thresholding Algorithms for Image Restoration","volume":"16","author":"Figueiredo","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"4293","DOI":"10.1109\/TSP.2017.2708040","article-title":"AMP-inspired deep networks for sparse linear inverse problems","volume":"65","author":"Borgerding","year":"2017","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"D32","DOI":"10.1364\/AO.52.000D32","article-title":"Fast lapped block reconstructions in compressive spectral imaging","volume":"52","author":"Arguello","year":"2013","journal-title":"Appl. Opt."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shi, Y.Q., and Sun, H. (1999). Image and Video Compression for Multimedia Engineering, CRC Press.","DOI":"10.1201\/9781420049794"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1073\/pnas.0437847100","article-title":"Optimally sparse representation in general (non- orthogonal) dictionaries via l1 minimization","volume":"100","author":"Donoho","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_32","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_33","unstructured":"Tropp, J.A., Wakin, M.B., Duarte, M.F., Baron, D., and Baraniuk, R.G. (2006, January 14\u201319). Random Filters for Compressive Sampling and Reconstruction. Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/JSTSP.2020.2977507","article-title":"Optimization-Inspired Compact Deep Compressive Sensing","volume":"14","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TIP.2009.2032889","article-title":"Variable Density Compressed Image Sampling","volume":"19","author":"Wang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","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":"Romberg","year":"2007","journal-title":"Inverse Probl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.sigpro.2016.11.024","article-title":"Projection matrix design using prior information in compressive sensing","volume":"135","author":"Li","year":"2017","journal-title":"Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.sigpro.2019.02.004","article-title":"Optimized structured sparse sensing matrices for compressive sensing","volume":"159","author":"Hong","year":"2019","journal-title":"Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MSP.2007.4286571","article-title":"Compressive Sensing [Lecture Notes]","volume":"24","author":"Baraniuk","year":"2007","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/TIT.2005.860430","article-title":"Stable recovery of sparse overcomplete representations in the presence of noise","volume":"52","author":"Donoho","year":"2005","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_42","unstructured":"Zhang, T. (2009). On the consistency of feature selection using greedy least squares regression. J. Mach. Learn. Res., 10."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TIT.2005.864420","article-title":"Just relax: Convex programming methods for identifying sparse signals in noise","volume":"52","author":"Tropp","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1093\/imanum\/8.1.141","article-title":"Two-Point Step Size Gradient Methods","volume":"8","author":"Barzilai","year":"1988","journal-title":"IMA J. Numer. Anal."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TIP.2011.2165289","article-title":"Kronecker Compressive Sensing","volume":"21","author":"Duarte","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/83.136597","article-title":"Image coding using wavelet transform","volume":"1","author":"Antonini","year":"1992","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Pearson. [3rd ed.]."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wei, Z., Zhang, J., Xu, Z., and Liu, Y. (2020). Optimization methods of compressively sensed image reconstruction based on single-pixel imaging. Appl. Sci., 10.","DOI":"10.3390\/app10093288"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1109\/LCOMM.2018.2877316","article-title":"SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency-Domain Sparsity","volume":"22","author":"Tung","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_51","unstructured":"(2021, March 06). Hyperspectral Color Imaging Repository. Available online: https:\/\/sites.google.com\/site\/hyperspectralcolorimaging\/dataset\/general-scenes."},{"key":"ref_52","unstructured":"(2021, March 15). TokyoTech Dataset. Available online: http:\/\/www.ok.sc.e.titech.ac.jp\/res\/MSI\/MSIdata31.html."},{"key":"ref_53","unstructured":"(2021, March 03). Real-World Hyperspectral Images Database. Available online: http:\/\/vision.seas.harvard.edu\/hyperspec\/download.html."},{"key":"ref_54","unstructured":"Akansu, A., and Medley, M. (1999). Wavelet, Subband and Block Transforms in Communications and Multimedia, Springer."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:08:04Z","timestamp":1760166484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,30]]},"references-count":54,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196551"],"URL":"https:\/\/doi.org\/10.3390\/s21196551","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,30]]}}}