Single pixel compressive spectral polarization imaging using a movable micro-polarizer array

Keywords: Stokes parameters, Compressive sensing, Spectral polarization images, Images reconstruction

Abstract

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.

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Author Biographies

Jorge Luis Bacca-Quintero, Industrial University of Santander

School of Systems Engineering and Informatics.

Héctor Miguel Vargas-García, Industrial University of Santander

School of Electrical, Electronic and Telecommunications Engineering.

Daniel Ricardo Molina-Velasco, Industrial University of Santander

School of Chemistry.

Henry Arguello-Fuentes, Industrial University of Santander

School of Systems Engineering and Informatics.

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.

Published
2018-09-18
How to Cite
Bacca-Quintero J. L., Vargas-García H. M., Molina-Velasco D. R., & Arguello-Fuentes H. (2018). Single pixel compressive spectral polarization imaging using a movable micro-polarizer array. Revista Facultad De Ingeniería Universidad De Antioquia, (88), 92-100. https://doi.org/10.17533/udea.redin.n88a10