Single pixel compressive spectral polarization imaging using a movable micro-polarizer array
DOI:
https://doi.org/10.17533/udea.redin.n88a10Keywords:
stokes parameters, compressive sensing, spectral polarization images, images reconstructionAbstract
The acquisition of spectral polarization images is a method that obtains polarized, spectral and spatial information of a scene. Traditional acquisition methods use dynamic elements that capture all the information of a scene, by scanning the areas of interest, which result in large amounts of data proportional to the desired image resolution. Hence, in this work, the compression of spectral polarization images using a single pixel architecture, that uses a micro-polarizer array aligned with a binary coded aperture is proposed. The micro-polarizer is moved horizontally in each shot, so that diverse types of codifications from the scene are obtained. The proposed architecture allows several compressive 2D projections with spatial, spectral and polarization coding to be obtained. Stokes parameter images at several wavelengths are reconstructed. This architecture reduces the total number of measurements needed to obtain spectral polarization images compared to traditional acquisition methods. The experiments validate the quality of the architecture obtaining 43.19 dB, 37.49 dB and 30.41 dB of the peak signal-to-noise ratio for the first three Stokes parameters respectively.
Downloads
References
J. B. Adams and A. R. Gillespie, Remote sensing of landscapes with spectral images: A physical modeling approach. Cambridge University Press, 2006.
Y. Zhao, C. Yi, S. G. Kong, Q. Pan, and Y. Cheng, Multi-band polarization imaging and applications. Springer, 2016.
C. Chen, Y.-q. Zhao, D. Liu, Q. Pan, and Y.-m. Cheng, “Polarization and spectral information jointly utilization in targets classification under different weather conditions,” in Photonics and Optoelectronic (SOPO), 2010 Symposium on. IEEE, 2010, pp. 1–4.
Y. Pu, W. Wang, G. Tang, F. Zeng, S. Achilefu, J. Vitenson, I. Sawczuk, S. Peters, J. Lombardo, and R. Alfano, “Spectral polarization imaging of human prostate cancer tissue using a near-infrared receptor-targeted contrast agent,” Technology in cancer research & treatment, vol. 4, no. 4, pp. 429–436, 2005.
S. M. Haugland, E. Bahar, and A. H. Carrieri, “Identification of contaminant coatings over rough surfaces using polarized infrared scattering,” Applied optics, vol. 31, no. 19, pp. 3847–3852, 1992.
Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Applied optics, vol. 48, no. 10, pp. D236–D246, 2009.
R. G. Sellar and G. D. Boreman, “Classification of imaging spectrometers for remote sensing applications,” Optical Engineering, vol. 44, no. 1, pp. 013 602–013 602, 2005.
K. P. Bishop, H. D. McIntire, M. P. Fetrow, and L. J. McMackin, “Multispectral polarimeter imaging in the visible to near ir,” in AeroSense’99. International Society for Optics and Photonics, 1999, pp. 49–57.
C. Fu, H. Arguello, B. M. Sadler, and G. R. Arce, “Compressive spectral polarization imaging by a pixelized polarizer and colored patterned detector,” JOSA A, vol. 32, no. 11, pp. 2178–2188, 2015.
F. Soldevila, E. Irles, V. Durán, P. Clemente, M. Fernández-Alonso, E. Tajahuerce, and J. Lancis, “Single-pixel polarimetric imaging spectrometer by compressive sensing,” Applied Physics B, vol. 113, no. 4, pp. 551–558, 2013.
J. Bacca, A. Guerrero, D. Molina, A. Camacho, and H. Arguello, “Compressive spectral polarization imaging using a single pixel detector,” in Computational Optical Sensing and Imaging. Optical Society of America, 2018, pp. CTu5D–2.
G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 105–115, 2014.
H. Arguello, H. Rueda, Y. Wu, D. W. Prather, and G. R. Arce, “Higher-order computational model for coded aperture spectral imaging,” Appl. Opt., vol. 52, no. 10, pp. D12–D21, Apr 2013. [Online]. Available: http://ao.osa.org/abstract.cfm?URI=ao-52-10-D12
J. Bacca, H. Vargas, and H. Arguello, “A constrained formulation for compressive spectral image reconstruction using linear mixture models,” in Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 IEEE 7th International Workshop on. IEEE, 2017, pp. 1–5.
H. G. Berry, G. Gabrielse, and A. Livingston, “Measurement of the stokes parameters of light,” Applied optics, vol. 16, no. 12, pp. 3200–3205, 1977.
J. R. Valenzuela, “Polarimetric image reconstruction algorithms,” Ph.D. dissertation, Michigan Tech, 2010.
F. A. Sadjadi and C. S. Chun, “Remote sensing using passive infrared stokes parameters,” Optical Engineering, vol. 43, no. 10, pp. 2283–2292, 2004.
M. F. Duarte and Y. C. Eldar, “Structured compressed sensing: From theory to applications,” IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4053–4085, 2011.
M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE Journal of selected topics in signal processing, vol. 1, no. 4, pp. 586–597, 2007.
C. V. Correa, H. Arguello, and G. R. Arce, “Spatiotemporal blue noise coded aperture design for multi-shot compressive spectral imaging,” JOSA A, vol. 33, no. 12, pp. 2312–2322, 2016.
J. A. M. Salazar, J. Bacca, and H. Arguello, “Compressive sensing matrix design using principal components analysis,” in Computational Optical Sensing and Imaging. Optical Society of America, 2017, pp. CTh1B–4.
N. E. Diaz, J. Bacca, and H. Arguello, “Gradient thresholding algorithm for adaptive colored coded aperture design in compressive spectral imaging,” in Computational Optical Sensing and Imaging. Optical Society of America, 2017, pp. JTu5A–4.
H. Arguello and G. R. Arce, “Colored coded aperture design by concentration of measure in compressive spectral imaging,” IEEE Transactions on Image Processing, vol. 23, no. 4, pp. 1896–1908, 2014.
H. Garcia, C. V. Correa, O. Villarreal, S. Pinilla, and H. Arguello, “Multi-resolution reconstruction algorithm for compressive single pixel spectral imaging,” in Signal Processing Conference (EUSIPCO), 2017 25th European. IEEE, 2017, pp. 468–472.
G. Warnell, S. Bhattacharya, R. Chellappa, and T. Başar, “Adaptive-rate compressive sensing using side information,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3846–3857, Nov 2015.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Revista Facultad de Ingeniería Universidad de Antioquia

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.