Spatial super-resolution in coded aperturebased optical compressive hyperspectral imaging systems
Keywords:super-resolution, hyper-spectral imaging, compressive sensing, optical imaging, CASSI, multi-shot, coded aperture-based systems
The Coded Aperture Snapshot Spectral Imaging system (CASSI) is a remarkable optical imaging architecture, which senses the spectral information of a three dimensional scene by using two-dimensional coded focal plane array (FPA) projections. The projections in CASSI are localized such that each measurement contains spectral information only from a specific spatial region of the data cube. Spatial resolution in CASSI is highly dependent on the resolution the FPA detector exhibits; hence, high-resolution images require high-resolution detectors that demand high costs. To overcome this problem, in this paper is proposed an optical model for spatial superresolution imaging called SR-CASSI. Spatial super-resolution is attained as an inverse problem from a set of low-resolution coded measurements by using a compressive sensing (CS) reconstruction algorithm. This model allows the reconstruction of spatially super-resolved hyper-spectral data cubes, where the spatial resolution is significantly enhanced. Simulation results show an improvement of up to 8 dB in PSNR when the proposed model is used.
R. Lin, B. Dennis, G. Hurford, D. Smith, A. Zehnder, P. Harvey, D. Curtis, D. Pankow, P. Turin, M. Bester, A. Csillaghy, M. Lewis, N. Madden, H. van Beek, M. Appleby, T. Raudorf, J. McTiernan, R. Ramaty, E. Schmahl, R. Schwartz, S. Krucker, R. Abiad, T. Quinn, P. Berg, M. Hashii, R. Sterling, R. Jackson, R. Pratt, R. Campbell, D. Malone, D. Landis, C. Barrington, S. Slassi, C. Cork, D. Clark, D. Amato, L. Orwig, R. Boyle, I. Banks, K. Shirey, A. Tolbert, D. Zarro, F. Snow, K. Thomsen, R. Henneck, A. McHedlishvili, P. Ming, M. Fivian, J. Jordan, R. Wanner, J. Crubb, J. Preble, M. Matranga, A. Benz, H. Hudson, R. Canfield, G. Holman, C. Crannell, T. Kosugi, A. Emslie, N. Vilmer, J. Brown, C. Johns-Krull, M. Aschwanden, T. Metcalf, A. Conway. “The Reuven Ramaty high-energy solar spectroscopic imager (RHESSI)”. Solar Physics. Vol. 210. 2002. pp. 3-32.
W. Smith, D. Zhou, F. Harrison, H. Revercomb, A. Larar, A. Huang, B. Huang. “Hyperspectral remote sensing of atmospheric profiles from satellites and aircraft”. Hyperspectral Remote Sensing of the Land and Atmosphere. Vol. 4151. 2001. pp. 94-102.
P. Ye, J. Paredes, G. Arce, Y. Wu, C. Chen, D. Prather. Compressive confocal microscopy. Proceeding of International Conference on Acoustics, Speech and Signal Processing. Taipei, Taiwan. 2009. pp. 429-432.
C. Stellman, F. Olchowski, J. Michalowicz. War horse (wide-area reconnaissance: hyperspectral overhead real-time surveillance experiment). Proceedings SPIE 4379, Automatic Target Recognition XI. Orlando, USA. Vol. 4379. 2001, pp. 339-346.
T. Pham, F. Bevilacqua, T. Spott, J. Dam, B. Tromberg, S. Andersson. “Quantifying the absorption and reduced scattering coefficients of tissue-like turbid media over a broad spectral range with noncontact fourier-transform hyperspectral imaging”. Applied Optics. Vol. 39. 2000. pp. 6487-6497.
D. Kittle. Compressive spectral imaging. Master’s thesis. Duke University. Durham, North Carolina, USA. 2010.
N. Hagen, R. Kester, L. Gao, T. Tkaczyk. “Snapshot advantage: a review of the light collection improvement for parallel high-dimensional measurement systems”. Optical Engineering. Vol. 51. 2011. pp. 111702-1 - 111702-7.
E. Candès, J. Romberg, T. Tao. “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information”. IEEE Transactions on Information Theory. Vol. 52. 2006. pp. 489-509.
E. Candès, T. Tao. “Near-optimal signal recovery from random projections: Universal encoding strategies?”. IEEE Transactions on Information Theory. Vol. 52. 2006. pp. 5406-5425.
D. Donoho. “Compressed sensing”. IEEE Transactions on Information Theory. Vol. 52. 2006. pp. 1289-1306.
E. Christophe, C. Mailhes, P. Duhamel. “Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3D wavelet coding”. IEEE Transactions on Image Processing. Vol. 17. 2008. pp. 2334-2346.
P. Dragotti, G. Poggi, A. Ragozini. “Compression of multispectral images by three-dimensional SPIHT algorithm”. IEEE Transactions on Geoscience and Remote Sensing. Vol. 38. 2000. pp. 416-428.
A. Wagadarikar, R. John, R. Willett, D. Brady. “Single disperser design for coded aperture snapshot spectral imaging”. Applied Optics. Vol. 47. 2008. pp. B44-B51.
H. Arguello, H. Rueda, Y. Wu, D. Prather, G. Arce, “Higher-order computational model for coded aperture spectral imaging.” Appl. Opt. Vol. 52. 2013. pp. D12-D21.
H. Arguello, C. Correa, G. Arce, “Fast lapped block reconstructions in compressive spectral imaging,” Appl. Opt. Vol. 52. 2013. pp. D32-D45.
Y. Wu, I. Mirza, G. Arce, D. Prather. “Development of a digital-micro-mirror-device-based multishot snapshot spectral imaging system”. Optics Letters. Vol. 36. 2011. pp. 2692-2694.
H. Arguello, G. Arce. “Code aperture optimization for spectrally agile compressive imaging”. Journal of the Optical Society of America A. Vol. 28. 2011. pp. 2400- 2413.
H. Arguello, C. Correa, G. Arce. “Code aperture optimization by concentration of measure in compressive spectral imaging”. Journal of the Optical Society of America A. USA. 2012.
D. Kittle, K. Choi, A. Wagadarikar, D. Brady. “Multi-frame image estimation for coded aperture snapshot spectral imagers”. Applied Optics. Vol. 49. 2010. pp. 6824-6833.
H. Arguello, G. Arce. Restricted Isometry Property in coded aperture compressive spectral imaging. IEEE Statistical Signal Processing Workshop. Ann Arbor, MI, USA. 2012. pp. 716-719.
H. Arguello, G. Arce. Spectrally Selective Compressive Imaging by Matrix Analysis. OSA Optics and Photonics Congress. Monterey, CA, USA. 2012. pp. CM4B.5.
H. Arguello, G. Arce. Code Aperture Agile Spectral Imaging (CAASI). Imaging and Applied Optics Congress (OSA Optics & Photonics Congress). Toronto, Canada, 2011. pp. ITuA4.
R. Willett, R. Marcia, J. Nichols. “Compressed sensing for practical optical imaging systems: A tutorial”. Optical Engineering. Vol. 50. 2011. pp. 072601 1-13.
M. Duarte, R. Baraniuk. Kronecker product matrices for compressive sensing. IEEE International Conference on Acoustics Speech and Signal Processing. Dallas, USA. 2010. pp. 3650-3653.
H. Arguello, G. Arce. “Rank minimization code aperture design for spectrally selective compressive imaging”. IEEE Transactions on Image Processing. Vol. 22. 2012. pp. 941-954.
M. Figueiredo, R. Nowak, S. Wright. “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems”. IEEE Journal of Selected Topics in Signal Processing. Vol. 1. 2007. pp. 586-597.
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