Synthetic coded apertures in compressive spectral imaging: Experimental validation

Coded aperture compressive spectral imagers allow capturing spectral imaging information of a 3D cube with just a single 2D measurement of the coded and spectrally dispersed source field. These imagers systems often rely on existing FPA detectors, SLMs, and micro mirror devices, which are often mismatched in pitch size and pixel resolution. A traditional solution consists on grouping several pixels in square features with the aim to find a match between them. As a result, the resolution of the reconstructions decreases significantly. To overcome these hardware constraints, this paper develops a new model by which the high resolution of the coding and detector elements are fully exploited. Real reconstructions show the improvement and resolution gain achieved with the proposed approach compared with the grouping-pixel traditional solution.

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

[2]  2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015, Orlando, FL, USA, December 14-16, 2015 , 2015, IEEE Global Conference on Signal and Information Processing.

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

[4]  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.

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

[6]  Henry Arguello,et al.  Synthetic coded apertures in compressive spectral imaging , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

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

[9]  Yin Zhang,et al.  A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing , 2012, IEEE Transactions on Image Processing.