Wednesday, December 10, 2014

Compressive Hyperspectral Imaging with Side Information

This is some interesting use of RGB information and matrix factorization for a hyperspectral system: Compressive Hyperspectral Imaging with Side Information by Xin Yuan, Tsung-Han Tsai, Ruoyu Zhu, Patrick Llull, David Brady, and Lawrence Carin


A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements. The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary in situ from the measurements via global-local shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information
 
 
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2 comments:

Wang Lizhi said...

This is some amazing work. it opens the door to use two cameras to capture hyperspectral images based on compressive sensing theory. Further it seems that single shot hyperspectral imaging always suffers from large distortion. So it is meaningful to employ two cameras to reach high quality.

Wang Lizhi said...

This is some amazing work. it opens the door to use two cameras to capture hyperspectral images based on compressive sensing theory. Further it seems that single shot hyperspectral imaging always suffers from large distortion. So it is meaningful to employ two cameras to reach high quality.

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