Colored coded apertures optimization in compressive spectral imaging by restricted isometry property

Coded Aperture Snapshot Spectral Imaging (CASSI) systems capture the spatial and spectral information of a scene by measuring 2D coded projections on a focal plane array (FPA). Compressed sensing reconstruction algorithms are then used to recover the underlying spectral data cube. The quality of the reconstructions in CASSI is determined by the design of a set of block-unblock coded apertures. In this work, the block-unblock coded apertures in CASSI are replaced by colored coded apertures. The Restricted Isometry Property (RIP) of the colored CASSI is developed and the structure of the colored coded apertures is designed such that the RIP is better satisfied. Simulations show significant gain in the quality of reconstructions for the optimized colored coded apertures over that attained by traditional block-unblock coded apertures.

[1]  Henry Arguello,et al.  Higher-order computational model for coded aperture spectral imaging. , 2013, Applied optics.

[2]  H. Rauhut Compressive Sensing and Structured Random Matrices , 2009 .

[3]  Mário A. T. Figueiredo,et al.  Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

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

[5]  Henry Arguello,et al.  Restricted Isometry Property in coded aperture compressive spectral imaging , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

[6]  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.

[7]  David J. Brady,et al.  Multiframe image estimation for coded aperture snapshot spectral imagers. , 2010, Applied optics.

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

[9]  Monson H. Hayes,et al.  Single image-based depth estimation using dual off-axis color filtered aperture camera , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Eunsung Lee,et al.  Multifocusing and Depth Estimation Using a Color Shift Model-Based Computational Camera , 2012, IEEE Transactions on Image Processing.

[11]  Henry Arguello,et al.  Fast lapped block reconstructions in compressive spectral imaging. , 2013, Applied optics.

[12]  Holger Rauhut,et al.  Nonuniform Sparse Recovery with Gaussian Matrices , 2010, ArXiv.

[13]  Henry Arguello,et al.  Rank Minimization Code Aperture Design for Spectrally Selective Compressive Imaging , 2013, IEEE Transactions on Image Processing.

[14]  Andreas rer. nat. Brückner,et al.  Microoptical multi aperture imaging systems , 2012 .

[15]  Manu Parmar,et al.  Selection of Optimal Spectral Sensitivity Functions for Color Filter Arrays , 2006, IEEE Transactions on Image Processing.

[16]  Holger Rauhut,et al.  Compressive Sensing with structured random matrices , 2012 .

[17]  Massimo Fornasier,et al.  Compressive Sensing and Structured Random Matrices , 2010 .

[18]  Ravindra Athale,et al.  Flexible multimodal camera using a light field architecture , 2009, 2009 IEEE International Conference on Computational Photography (ICCP).

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

[20]  Henry Arguello,et al.  Colored Coded Aperture Design by Concentration of Measure in Compressive Spectral Imaging , 2014, IEEE Transactions on Image Processing.