Publicado

2014-11-01

Efficient reconstruction of Raman spectroscopy imaging based on compressive sensing

Reconstrucción eficiente de imágenes a partir de espectroscopia Raman basada en la técnica de sensado compresivo

Palabras clave:

Raman Spectroscopy, Spectral Imaging, Compressed Sensing, Coded Aperture. (en)
Espectroscopia Raman, Imágenes Espectrales, Sensado Compresivo, Aperturas Codificadas. (es)

Autores/as

  • Diana Fernanda Galvis-Carreño Universidad Industrial de Santander - Escuela de Ingeniería Química
  • Yuri Hercilia Mejía-Melgarejo Universidad Industrial de Santander - Escuela de Ingenierías Eléctrica, Electrónica y de Telecomunicaciones
  • Henry Arguello-Fuentes Universidad Industrial de Santander - Escuela de Ingeniería de Sistemas e Informática
Raman Spectroscopy Imaging requires long periods of time for the data acquisition and subsequent treatment of the spectral chemical images. Recently, Compressed Sensing (CS) technique has been used satisfactorily in Raman Spectroscopy Imaging, reducing the acquisition time by simultaneously sensing and compressing the underlying Raman spectral signals. The Coded Aperture Snapshot Spectral Imager (CASSI) is an optical architecture that applied effectively the CS technique in Raman Spectroscopy Imaging. The main optical element of CASSI system is a coded aperture, which can transmit or block the information from the underlying scene. The principal design variable in the coded apertures is the percentage of transmissive elements or transmittance. This paper describes the technique of CS in Raman Spectroscopy imaging by using the CASSI system and realizes the selection of the optimal transmittance values of the coded apertures to ensure an efficient recovery of Raman Images. Diverse simulations are performed to determine the Peak Signal to Noise Ratio (PSNR) of the reconstructed Raman data cubes as a function of the transmittance of the coded apertures, the size of the underlying Raman data cubes and the number of projections expressed in terms of the compression ratio.
La Espectroscopia Raman de Imágenes requiere largos periodos de tiempo en la adquisición como en el tratamiento de datos para la construcción de imágenes químicas. Para reducir el tiempo se ha empleado la técnica de Sensado Compresivo (SC) gracias a la detección y compresión simultánea de las señales. El sistema de adquisición de imágenes basado en una apertura codificada (CASSI) es una arquitectura óptica que aplica de manera eficiente los conceptos de SC. El principal elemento del sistema CASSI es una apertura codificada, la cual puede ser vista como un filtro que transmite o bloquea información de una escena. El porcentaje de elementos transmisores es conocido como la transmitancia y esta es una variable de diseño. Este trabajo describe la técnica de SC aplicada a la Espectroscopia Raman de Imágenes empleando el sistema CASSI y realiza la selección de los valores óptimos de transmitancia que garantizan una eficiente reconstrucción de imágenes. Se realizaron diversas simulaciones para determinar la relación señal a ruido (PSNR) de la reconstrucción de un cubo de datos Raman como función de la transmitancia, el tamaño del cubo y el número de capturas expresadas en términos de la relación de compresión.

Referencias

Yaohai, L., Guangming, S., Dahua, G. and Danhua, L. High-resolution spectral imaging based on coded dispersion. Applied Optics, vol. 52 (5), pp. 1041-1049, 2013.

Wagadarikar, A., John, R., Willett, R. and Brady, D. Single disperser design for coded aperture snapshot spectral imaging. Applied Optics, vol. 47 (10), pp. 44-51, 2008. https://doi.org/10.1364/AO.47.000B44

Lau, D., Villis, C., Furman, S. and Livett, M. Multispectral and hyperspectral image analysis of elemental and micro-Raman maps of cross-sections from a 16th century painting. Analytica Chimica Acta. vol. 610 (1), pp. 15-24, 2008. https://doi.org/10.1016/j.aca.2007.12.043

McCain, S. T., Gehm, M. E., Wang, Y., Pitsianis, N. P., Brady, D. J. Coded Aperture Raman Spectroscopy for quantitative measurements of ethanol in a tissue phantom. Applied Spectroscopy, vol. 60 (6), pp. 663-671, 2006. https://doi.org/10.1366/000370206777670693

Majzner, K., Kaczor, A., Kachamakova- Trojanowska, N., Fedorowicz, A., Chlopicki, S. and Baranska, M. 3D confocal Raman imaging of endothelial cells and vascular wall. Perspectives in analytical spectroscopy of biomedical research. Analyst, vol. 138 (2), pp. 603-610, 2013. https://doi.org/10.1039/c2an36222h

Hagen, N. and Brady, D. Coded Aperture DUV spectrometer for standoff Raman Spectroscopy. Proc. SPIE 7319, Next Generation Spectroscopic Technologies II, vol. 7319, 2009.

McCain, S. T., Gehm, M. E., Wang, Y., Pitsianis, N. P. and Brady, D. J. Multimodal multiplex Raman Spectroscopy optimized for in vivo chemometrics. Biomedical vibrational Spectroscopy III: Advances in Research and Industry, pp. 1-8, 2006.

Davis, B. M., Hemphill, A. J., Maltas, D. C., Zipper, M. A., Wang, P. and Ben-Amotz, D. Multivariate Hyperspectral Raman Imaging Using Compressive Detection. Analytical Chemistry, vol. 83 (12), pp. 5086-5092, 2011. https://doi.org/10.1021/ac103259v

Schlücker, S., Schaeberle, M. D., Huffman, S. W. and Levin, I. W. Raman Microspectroscopy: a comparison of point, line and wide-field imaging methodologies. Analytical Chemistry, vol. 75 (16), pp. 4312-4318, 2003.

Hagen N., Kester R., Gao L. and Tkackyk T. Snapshot advantage: a review of the light collection improvement for parallel high-dimensional measurement systems. Optical Engineering, vol. 51 (11), pp. 111702 1-7, 2012.

Donoho, D. Compressed Sensing. IEEE Transactions on Information Theory, vol. 52 (4), pp. 1289-1306, 2006. https://doi.org/10.1109/TIT.2006.871582

Candes, E., Romberg, J. and Tao, T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, vol. 52 (2), pp. 489-509, 2006. https://doi.org/10.1109/TIT.2005.862083

Willet, R., Marcia, R. and Nichols, J. Compressed Sensing for Practical Optical Imaging Systems: a tutorial. Optical Engineering, vol. 50 (7), 2011.

Arguello, H. and Arce, G. R. Rank minimization Coded Aperture design for spectrally selective Compressive Imaging. IEEE Transactions on Image Processing, vol. 22 (3), pp. 941-954, 2013. https://doi.org/10.1109/TIP.2012.2222899

Joya, M., Barba. J. and Pizani, P. Efectos estructurales en el semiconductor INSB, por la aplicación de diferentes métodos de presión. Dyna, vol. 79 (15), pp. 137-141, 2012.

Abramczyk, H. and Brozek-Pluska, B. Raman Imaging in Biochemical and Biomedical Applications. Diagnosis and Treatment of Breast Cancer. To appear in Chemical Reviews, 2014.

Mogilevsky, G., Borland, L., Brickhouse, M. and Fountain, A. W. Raman Spectroscopy for Homeland Security Applications. International Journal of Spectroscopy [Online], 2012. . [Date of reference July 25th of 2013]. Available at: http://www.hindawi.com/journals/ijs/2012/808079/

Maltaşa, D. C., Kwokb, K., Wanga, P., Taylorb, L. S. and Ben-Amotz, D. Rapid classification of pharmaceutical ingredients with Raman spectroscopy using compressive detection strategy with PLS-DA multivariate filters. Journal of Pharmaceutical and Biomedical Analysis, vol. 80, pp. 63-68, 2013.

Donoho, D., Tsaig, Y., Drori, I. and Starck, J. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Transactions on Information Theory, vol. 58 (2) pp. 1094-1121, 2012. https://doi.org/10.1109/TIT.2011.2173241

Duarte, M. and Baraniuk, R. Kronecker Compressive Sensing. IEEE Transactions on Image Processing, vol. 21 (2), pp. 494-504, 2012. https://doi.org/10.1109/TIP.2011.2165289

Arguello, H. and Arce, G. R. Code Aperture Optimization for Spectrally Agile Compressive Imaging. Journal of the Optical Society of America A, vol. 23 (11), pp. 2400-2413, 2011. https://doi.org/10.1364/JOSAA.28.002400

Arguello, H., Rueda, H., Wu, Y., Prather, D. Arce, G. R. Higher-order computational model for coded aperture spectral imaging. Applied Optics, vol. 52 (10), pp. D12- D21, 2012. https://doi.org/10.1364/AO.52.000D12

Arce, G. R., Brady, D. J., Carin, L. and Arguello, H. Compressive Coded Aperture Spectral Imaging: An Introduction. IEEE Signal Processing Magazine, vol. 31 (1), pp. 105-115, 2014. https://doi.org/10.1109/MSP.2013.2278763

Rueda, H. and Arguello, H. Spatial super- resolution in coded aperture-based optical compressive hyperspectral imaging systems. Revista Facultad de Ingeniería Universidad de Antioquia, pp. 7-18, 2013.

Wagadarikar, A., Pitsianis, N. P., Sun, X. and Brady, D. J. Spectral image estimation for coded aperture snapshot spectral imagers. Proceedings of SPIE, vol. 7076, pp. 707602-707615, 2008. https://doi.org/10.1117/12.795545

Bioucas-Dias, J. and Figueiredo, M. A new TwIST: Two-step iterative shrinking/thresholding algorithms for image restoration. IEEE Transactions Image Processing, vol. 16, pp. 2992-3004, 2007. https://doi.org/10.1109/TIP.2007.909319

Arguello, H. and Arce, G. R. Restricted Isometry Property in Coded Aperture Compressive Spectral Imaging. IEEE Statistical Signal Processing Workshop, Ann Arbor, MI, USA, 2012.

Arguello, H., Correa, C. V. and Arce, G. R Fast lapped block reconstructions in compressive spectral imaging. Applied Optics, vol. 52 (10), pp. D32-D45, 2013. https://doi.org/10.1364/AO.52.000D32