{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:27:00Z","timestamp":1760239620340,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T00:00:00Z","timestamp":1607472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu postdoctoral research funding pr ogram","award":["2019K216"],"award-info":[{"award-number":["2019K216"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61727802"],"award-info":[{"award-number":["61727802"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u9752\u5e74\u57fa\u91d1","award":["61901220"],"award-info":[{"award-number":["61901220"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Spectral detection provides rich spectral\u2013temporal information with wide applications. In our previous work, we proposed a dual-path sub-Hadamard-s snapshot Hadamard transform spectrometer (Sub-s HTS). In order to reduce the complexity of the system and improve its performance, we present a convolution neural network-based method to recover the light intensity distribution from the overlapped dispersive spectra, rather than adding an extra light path to capture it directly. In this paper, we construct a network-based single-path snapshot Hadamard transform spectrometer (net-based HTS). First, we designed a light intensity recovery neural network (LIRNet) with an unmixing module (UM) and an enhanced module (EM) to recover the light intensity from the dispersive image. Then, we used the reconstructed light intensity as the original light intensity to recover high signal-to-noise ratio spectra successfully. Compared with Sub-s HTS, the net-based HTS has a more compact structure and high sensitivity. A large number of simulations and experimental results have demonstrated that the proposed net-based HTS can obtain a better-reconstructed signal-to-noise ratio spectrum than the Sub-s HTS because of its higher light throughput.<\/jats:p>","DOI":"10.3390\/s20247038","type":"journal-article","created":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T09:17:58Z","timestamp":1607505478000},"page":"7038","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8336-2845","authenticated-orcid":false,"given":"Hui","family":"Xie","sequence":"first","affiliation":[{"name":"School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Zhuang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jing","family":"Han","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Lianfa","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,9]]},"reference":[{"key":"ref_1","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":"Candes","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_2","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_3","first-page":"62460A-1","article-title":"Compressive imaging spectrometers using coded apertures","volume":"6246","author":"Brady","year":"2006","journal-title":"Vis. Inf. Process."},{"key":"ref_4","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_5","unstructured":"Donoho, D., Tsaig, Y., Drori, I., and Starck, J.L. (2006). Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit, Department of Statistics, Stanford University. Technical Report."},{"key":"ref_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MSP.2016.2582378","article-title":"Computational snapshot multispectral cameras: Toward dynamic capture of the spectral world","volume":"33","author":"Cao","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4595","DOI":"10.1109\/TSP.2011.2161292","article-title":"The in-crowd algorithm for fast basis pursuit denoising","volume":"59","author":"Gill","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/cpa.20042","article-title":"An iterative thresholding algorithm forlinear inverse problems with a sparsity constraint","volume":"57","author":"Daubechies","year":"2004","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2992","DOI":"10.1109\/TIP.2007.909319","article-title":"A new TwIST: Two-step iterative shrinking\/thresholding algorithms for image restoration","volume":"16","author":"Figueiredo","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","unstructured":"Chartrand, R., and Yin, W. (April, January 31). Iteratively reweighted algorithms for compressive sensing. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Las Vegas, NV, USA."},{"key":"ref_13","first-page":"211","article-title":"Sparse bayesian learning and the relevance vector machine","volume":"1","author":"Tipping","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_14","unstructured":"Tipping, M.E., and Faul, A.C. (2003, January 1). Fast marginal likelihood maximization for sparse bayesian models. Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics, Key West, FL, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Schniter, P., Potter, L.C., and Ziniel, J. (2008, January 1). Fast bayesian matching pursuit. Proceedings of the Workshop on Information Theory and Applications, La Jolla, CA, USA.","DOI":"10.1109\/ITA.2008.4601068"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4932","DOI":"10.1364\/AO.46.004932","article-title":"Performance comparison of aperture codes for multimodal, multiplex spectroscopy","volume":"46","author":"Wagadarikar","year":"2007","journal-title":"Appl. Opt."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1364\/AO.45.002965","article-title":"Static two-dimensional aperture coding for multimodal, multiplex spectroscopy","volume":"45","author":"Gehm","year":"2006","journal-title":"Appl. Opt."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5742","DOI":"10.1364\/OE.15.005742","article-title":"Longwave infrared (LWIR) coded aperture dispersive spectrometer","volume":"15","author":"Fernandez","year":"2007","journal-title":"Opt. Express"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124322","DOI":"10.1016\/j.optcom.2019.124322","article-title":"High-SNR snapshot multiplex spectrometer with sub-Hadamard-S matrix coding","volume":"453","author":"Zhao","year":"2019","journal-title":"Opt. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.1364\/OL.39.003744","article-title":"Denoising analysis of hadamard transform spectrometry","volume":"39","author":"Yue","year":"2014","journal-title":"Opt. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.optcom.2017.09.072","article-title":"Denoising analysis of spatial pixel multiplex coded spectrometer with hadamard, H.-matrix","volume":"407","author":"Yue","year":"2018","journal-title":"Opt. Commun."},{"key":"ref_22","unstructured":"Wu, Z., Shen, C., and van den Hengel, A. (2016). High-performance semantic segmentation using very deep fully convolutional networks. arXiv."},{"key":"ref_23","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","article-title":"ERFNet: Efficient residual factorized ConvNet for real-time semantic segmentation","volume":"19","author":"Romera","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6824","DOI":"10.1364\/AO.49.006824","article-title":"Multiframe image estimation for coded aperture snapshot spectral imagers","volume":"49","author":"Kittle","year":"2010","journal-title":"Appl. Opt."},{"key":"ref_26","unstructured":"(2020, December 05). Indian Pines. Available online: http:\/\/lesun.weebly.com\/hyperspectral-data-set.html."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chakrabarti, A., and Zickler, T. (2011, January 23). Statistics of real-world hyperspectral images. Proceedings of the IEEE Confernence on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995660"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7038\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:42:37Z","timestamp":1760179357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,9]]},"references-count":27,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247038"],"URL":"https:\/\/doi.org\/10.3390\/s20247038","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,12,9]]}}}