Multitrack Compressed Sensing for Faster Hyperspectral Imaging
Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensin...
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| Published in: | Sensors (Basel, Switzerland) Vol. 21; no. 15; p. 5034 |
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| Main Authors: | , , , |
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| Abstract | Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times. |
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| AbstractList | Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times. Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times.Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times. |
| Author | Kubal, Sharvaj Tay, Chor Yong Yong, Derrick Lee, Elizabeth |
| AuthorAffiliation | 4 School of Biological Sciences, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore 1 Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore; sharvaj.kubal@smart.mit.edu 2 Critical Analytics for Manufacturing Personalized-Medicine, Singapore-MIT Alliance for Research and Technology Centre, 1 Create Way, Singapore 138602, Singapore; elizabeth.lee@smart.mit.edu 3 School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore; cytay@ntu.edu.sg |
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| References | Aharon (ref_20) 2006; 54 Wagadarikar (ref_12) 2008; 47 Cao (ref_14) 2016; 33 Wang (ref_25) 2019; 28 Kittle (ref_35) 2010; 49 Magalhaes (ref_36) 2012; 51 Rani (ref_18) 2018; 6 ref_11 Gehm (ref_32) 2007; 15 Goetz (ref_4) 2009; 113 Wagadarikar (ref_34) 2009; 17 ref_17 ref_16 ref_15 ref_37 Fisher (ref_6) 2019; 37 Marques (ref_19) 2019; 7 Averbuch (ref_27) 2012; 5 Masia (ref_45) 2015; 46 Choi (ref_21) 2017; 36 Hagen (ref_8) 2013; 52 Rousset (ref_39) 2018; 26 Arce (ref_13) 2014; 31 Dai (ref_28) 2014; 53 Dale (ref_3) 2013; 48 Lu (ref_1) 2014; 19 ref_24 ref_46 Mistretta (ref_7) 2010; 20 ref_23 ref_22 ref_44 ref_43 ref_42 ref_40 Donoho (ref_10) 2006; 52 Lu (ref_30) 2020; 56 Huang (ref_31) 2015; 66 Hahn (ref_38) 2014; 26 Candes (ref_9) 2006; 52 Duarte (ref_33) 2008; 25 ref_26 Rousset (ref_29) 2017; 3 ref_5 Fowler (ref_41) 2012; 4 Li (ref_2) 2010; 42 |
| References_xml | – volume: 48 start-page: 142 year: 2013 ident: ref_3 article-title: Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review publication-title: Appl. Spectrosc. Rev. doi: 10.1080/05704928.2012.705800 – volume: 3 start-page: 36 year: 2017 ident: ref_29 article-title: Adaptive Basis Scan by Wavelet Prediction for Single-Pixel Imaging publication-title: IEEE Trans. Comput. Imag. doi: 10.1109/TCI.2016.2637079 – volume: 19 start-page: 010901 year: 2014 ident: ref_1 article-title: Medical hyperspectral imaging: A review publication-title: J. Biomed. Opt. doi: 10.1117/1.JBO.19.1.010901 – volume: 113 start-page: S5 year: 2009 ident: ref_4 article-title: Three decades of hyperspectral remote sensing of the Earth: A personal view publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2007.12.014 – volume: 15 start-page: 14013 year: 2007 ident: ref_32 article-title: Single-shot compressive spectral imaging with a dual-disperser architecture publication-title: Opt. Express doi: 10.1364/OE.15.014013 – ident: ref_5 – volume: 56 start-page: 312 year: 2020 ident: ref_30 article-title: Smart manufacturing process and system automation—A critical review of the standards and envisioned scenarios publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2020.06.010 – volume: 42 start-page: 1010 year: 2010 ident: ref_2 article-title: Detection of physical defects in solar cells by hyperspectral imaging technology publication-title: Opt. Laser Technol. doi: 10.1016/j.optlastec.2010.01.022 – ident: ref_26 – volume: 20 start-page: 1009 year: 2010 ident: ref_7 article-title: Applied advanced process analytics in biopharmaceutical manufacturing: Challenges and prospects in real-time monitoring and control publication-title: J. Process Control doi: 10.1016/j.jprocont.2010.05.008 – ident: ref_44 doi: 10.1007/978-3-319-46478-7_2 – volume: 26 start-page: 10550 year: 2018 ident: ref_39 article-title: Time-resolved multispectral imaging based on an adaptive single-pixel camera publication-title: Opt. Express doi: 10.1364/OE.26.010550 – ident: ref_43 doi: 10.1364/FIO.2020.JTu1B.28 – ident: ref_40 – volume: 31 start-page: 105 year: 2014 ident: ref_13 article-title: Compressive Coded Aperture Spectral Imaging: An Introduction publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2013.2278763 – ident: ref_23 doi: 10.1109/ITOEC.2017.8122510 – volume: 49 start-page: 6824 year: 2010 ident: ref_35 article-title: Multiframe image estimation for coded aperture snapshot spectral imagers publication-title: Appl. Opt. doi: 10.1364/AO.49.006824 – ident: ref_37 – ident: ref_42 – volume: 17 start-page: 6368 year: 2009 ident: ref_34 article-title: Video rate spectral imaging using a coded aperture snapshot spectral imager publication-title: Opt. Express doi: 10.1364/OE.17.006368 – volume: 25 start-page: 83 year: 2008 ident: ref_33 article-title: Single-pixel imaging via compressive sampling publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2007.914730 – ident: ref_17 doi: 10.1145/3306346.3322946 – ident: ref_16 doi: 10.1145/3130800.3130896 – volume: 7 start-page: 1300 year: 2019 ident: ref_19 article-title: A Review of Sparse Recovery Algorithms publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2886471 – ident: ref_22 doi: 10.1109/ALLERTON.2015.7447163 – volume: 52 start-page: 1289 year: 2006 ident: ref_10 article-title: Compressed sensing publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2006.871582 – ident: ref_24 doi: 10.1109/ICCVW.2017.68 – volume: 52 start-page: 489 year: 2006 ident: ref_9 article-title: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2005.862083 – volume: 5 start-page: 57 year: 2012 ident: ref_27 article-title: Adaptive Compressed Image Sensing Using Dictionaries publication-title: SIAM J. Imag. Sci. doi: 10.1137/110820579 – volume: 26 start-page: 113 year: 2014 ident: ref_38 article-title: Compressive sensing and adaptive direct sampling in hyperspectral imaging publication-title: Digit. Signal Process. doi: 10.1016/j.dsp.2013.12.001 – ident: ref_11 doi: 10.1201/9781315371474 – ident: ref_46 – ident: ref_15 doi: 10.1364/COSI.2009.CTuA5 – volume: 53 start-page: 6619 year: 2014 ident: ref_28 article-title: Adaptive compressed sampling based on extended wavelet trees publication-title: Appl. Opt. doi: 10.1364/AO.53.006619 – volume: 51 start-page: 1 year: 2012 ident: ref_36 article-title: High-resolution hyperspectral single-pixel imaging system based on compressive sensing publication-title: Opt. Eng. doi: 10.1117/1.OE.51.7.071406 – volume: 46 start-page: 727 year: 2015 ident: ref_45 article-title: Hyperspectral image analysis for CARS, SRS, and Raman data publication-title: J. Raman Spectrosc. doi: 10.1002/jrs.4729 – volume: 52 start-page: 1 year: 2013 ident: ref_8 article-title: Review of snapshot spectral imaging technologies publication-title: Opt. Eng. doi: 10.1117/1.OE.52.9.090901 – volume: 28 start-page: 2257 year: 2019 ident: ref_25 article-title: HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2018.2884076 – volume: 37 start-page: 253 year: 2019 ident: ref_6 article-title: The Current Scientific and Regulatory Landscape in Advancing Integrated Continuous Biopharmaceutical Manufacturing publication-title: Trends Biotechnol. doi: 10.1016/j.tibtech.2018.08.008 – volume: 6 start-page: 4875 year: 2018 ident: ref_18 article-title: A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2793851 – volume: 36 start-page: 1 year: 2017 ident: ref_21 article-title: High-quality hyperspectral reconstruction using a spectral prior publication-title: ACM Trans. Graph. doi: 10.1145/3130800.3130810 – volume: 47 start-page: B44 year: 2008 ident: ref_12 article-title: Single disperser design for coded aperture snapshot spectral imaging publication-title: Appl. Opt. doi: 10.1364/AO.47.000B44 – volume: 54 start-page: 4311 year: 2006 ident: ref_20 article-title: K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2006.881199 – volume: 33 start-page: 95 year: 2016 ident: ref_14 article-title: Computational Snapshot Multispectral Cameras: Toward dynamic capture of the spectral world publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2016.2582378 – volume: 66 start-page: 1 year: 2015 ident: ref_31 article-title: Automated visual inspection in the semiconductor industry: A survey publication-title: Comput. Ind. doi: 10.1016/j.compind.2014.10.006 – volume: 4 start-page: 297 year: 2012 ident: ref_41 article-title: Block-Based Compressed Sensing of Images and Video publication-title: Found. Trends Signal Process. doi: 10.1561/2000000033 |
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