SNAPSHOT SPECTRAL AND COLOR IMAGING USING A REGULAR DIGITAL CAMERA WITH A MONOCHROMATIC IMAGE SENSOR

Abstract. Spectral imaging (SI) refers to the acquisition of the three-dimensional (3D) spectral cube of spatial and spectral data of a source object at a limited number of wavelengths in a given wavelength range. Snapshot spectral imaging (SSI) refers to the instantaneous acquisition (in a single shot ) of the spectral cube, a process suitable for fast changing objects. Known SSI devices exhibit large total track length (TTL), weight and production costs and relatively low optical throughput. We present a simple SSI camera based on a regular digital camera with (i) an added diffusing and dispersing phase-only static optical element at the entrance pupil ( diffuser ) and (ii) tailored compressed sensing (CS) methods for digital processing of the diffused and dispersed (DD) image recorded on the image sensor. The diffuser is designed to mix the spectral cube data spectrally and spatially and thus to enable convergence in its reconstruction by CS-based algorithms. In addition to performing SSI, this SSI camera is capable to perform color imaging using a monochromatic or gray-scale image sensor without color filter arrays.

[1]  Vasilis Ntziachristos,et al.  Multispectral imaging using multiple-bandpass filters. , 2008, Optics letters.

[2]  D. Foster,et al.  Frequency of metamerism in natural scenes , 2006 .

[3]  R. M. Willett,et al.  Compressed sensing for practical optical imaging systems: A tutorial , 2011, IEEE Photonics Conference 2012.

[4]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[5]  Javier Hernández-Andrés,et al.  Developing an optimum computer-designed multispectral system comprising a monochrome CCD camera and a liquid-crystal tunable filter. , 2008, Applied optics.

[6]  Bahram Javidi,et al.  Optically compressed image sensing using random aperture coding , 2008, SPIE Defense + Commercial Sensing.

[7]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[8]  D. Brady Optical Imaging and Spectroscopy , 2009 .

[9]  E. Frenkel,et al.  Molecular profiling of individual tumor cells by hyperspectral microscopic imaging. , 2012, Translational research : the journal of laboratory and clinical medicine.

[10]  Amir Averbuch,et al.  Spline and Spline Wavelet Methods with Applications to Signal and Image Processing , 2014 .

[11]  Jian-Feng Cai,et al.  Split Bregman Methods and Frame Based Image Restoration , 2009, Multiscale Model. Simul..

[12]  Haida Liang,et al.  Advances in multispectral and hyperspectral imaging for archaeology and art conservation , 2012 .

[13]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[14]  Ashwin A. Wagadarikar,et al.  Single disperser design for coded aperture snapshot spectral imaging. , 2008, Applied optics.

[15]  Matthias F. Carlsohn Spectral image processing in real-time , 2006, Journal of Real-Time Image Processing.

[16]  Colin Victor. Greensill,et al.  Sugar “Imaging” of Fruit Using a Low Cost Charge-Coupled Device Camera , 2005 .

[17]  Alistair Gorman,et al.  Generalization of the Lyot filter and its application to snapshot spectral imaging. , 2010, Optics express.

[18]  Xin Yuan,et al.  Compressive Hyperspectral Imaging With Side Information , 2015, IEEE Journal of Selected Topics in Signal Processing.

[19]  Henry Arguello Fuentes,et al.  Spectral selectivity in compressive spectral imaging based on grayscale coded apertures , 2013, Symposium of Signals, Images and Artificial Vision - 2013: STSIVA - 2013.

[20]  Amir Averbuch,et al.  Compressed sensing snapshot spectral imaging by a regular digital camera with an added optical diffuser. , 2016, Applied optics.

[21]  Amir Averbuch,et al.  Delineation of malignant skin tumors by hyperspectral imaging using diffusion maps dimensionality reduction , 2015, Biomed. Signal Process. Control..

[22]  Zuowei Shen Wavelet Frames and Image Restorations , 2011 .

[23]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[24]  Yuval Garini,et al.  Spectral imaging: Principles and applications , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[25]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.