Measurement dimensions compressed spectral imaging with a single point detector

Abstract An experimental demonstration of spectral imaging with measurement dimensions compressed has been performed. With the method of dual compressed sensing (CS) we derive, the spectral image of a colored object can be obtained with only a single point detector, and sub-sampling is achieved in both spatial and spectral domains. The performances of dual CS spectral imaging are analyzed, including the effects of dual modulation numbers and measurement noise on the imaging quality. Our scheme provides a stable, high-flux measurement approach of spectral imaging.

[1]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[2]  J. CandesE.,et al.  Robust uncertainty principles , 2006 .

[3]  J. Howell,et al.  Photon-counting compressive sensing laser radar for 3D imaging. , 2011, Applied optics.

[4]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[5]  Richard G. Baraniuk,et al.  Sparsity and Structure in Hyperspectral Imaging , 2014 .

[6]  Wenlin Gong,et al.  Ghost imaging lidar via sparsity constraints , 2012, 1203.3835.

[7]  Qionghai Dai,et al.  Dual-coded compressive hyperspectral imaging. , 2014, Optics letters.

[8]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[9]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[10]  Marco F. Duarte,et al.  Spectral compressive sensing , 2013 .

[11]  Henry Arguello,et al.  Compressive Coded Aperture Spectral Imaging: An Introduction , 2014, IEEE Signal Processing Magazine.

[12]  Wenlin Gong,et al.  Sub-Rayleigh ghost imaging via sparsity constraints based on a digital micro-mirror device , 2013 .

[13]  J. CandesE.,et al.  Near-Optimal Signal Recovery From Random Projections , 2006 .

[14]  Richard G. Baraniuk,et al.  A new compressive imaging camera architecture using optical-domain compression , 2006, Electronic Imaging.

[15]  Enrique Tajahuerce,et al.  Single-pixel polarimetric imaging spectrometer by compressive sensing , 2013 .

[16]  Shen Li,et al.  Protocol based on compressed sensing for high-speed authentication and cryptographic key distribution over a multiparty optical network. , 2013, Applied optics.

[17]  D. L. Donoho,et al.  Compressed sensing , 2006, IEEE Trans. Inf. Theory.

[18]  Richard G. Baraniuk,et al.  Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection , 2014, IEEE Signal Processing Magazine.

[19]  Wen-Kai Yu,et al.  Single-photon compressive imaging with some performance benefits over raster scanning , 2014 .

[20]  Shree K. Nayar,et al.  A theory of multiplexed illumination , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[21]  Dennis W Prather,et al.  Development of a digital-micromirror-device-based multishot snapshot spectral imaging system. , 2011, Optics letters.

[22]  Henry Arguello,et al.  Code aperture design for compressive spectral imaging , 2010, 2010 18th European Signal Processing Conference.

[23]  Chengbo Li.  An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing , 2010 .

[24]  Adrian Stern,et al.  Compressed Imaging With a Separable Sensing Operator , 2009, IEEE Signal Processing Letters.

[25]  Henry Arguello,et al.  Code aperture optimization for spectrally agile compressive imaging. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  Richard G. Baraniuk,et al.  Kronecker Compressive Sensing , 2012, IEEE Transactions on Image Processing.

[27]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

[28]  Jianwei Ma,et al.  Single-Pixel Remote Sensing , 2009, IEEE Geoscience and Remote Sensing Letters.

[29]  Adrian Stern,et al.  Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains. , 2013, Applied optics.

[30]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[31]  K. Kelly,et al.  Compressive Sensing Hyperspectral Imager , 2009 .