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 globallocal 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.Index Terms-Compressive sensing, hyperspectral image, side information, Bayesian shrinkage, dictionary learning, blind compressive sensing, computational photography, coded aperture snapshot spectral imaging (CASSI), spatial light modulation.
Blind compressive sensing (CS) is considered for reconstruction of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition of the coded wavelengthdependent data, with the ambient three-dimensional hyperspectral datacube mapped to a two-dimensional measurement. The hyperspectral datacube is recovered using a Bayesian implementation of blind CS.Several demonstration experiments are presented, including measurements performed using a coded aperture snapshot spectral imager (CASSI) camera. The proposed approach is capable of efficiently reconstructing large hyperspectral datacubes. Comparisons are made between the proposed algorithm and other techniques employed in compressive sensing, dictionary learning and matrix factorization.
Index Termshyperspectral images, image reconstruction, projective transformation, dictionary learning, non-parametric Bayesian, Beta-Bernoulli model, coded aperture snapshot spectral imager (CASSI).
Abstract-The paradigm shift toward SDN has exhibited the following trends: (1) relying on a centralized and more powerful controller to make intelligent decisions, and (2) allowing a set of relatively dumb switches to route packets. Therefore, efficiently looking up the flowtables in forwarding switches to guarantee low latency becomes a critical issue. In this paper, following the similar paradigm, we propose a new routing scheme called KeySet which is flowtable-free and enables constant-time switching at the forwarding switches. Instead of looking up long flowtables, KeySet relies on a residual system to quickly calculate routing paths. A switch only needs to do simple modular arithmetics to obtain a packet's forwarding output port. Moreover, KeySet has a nice fault-tolerant capability because in many cases the controller does not need to update flowtables at switches when a failure occurs. We validate KeySet through extensive simulations by using general as well as Facebook fat-tree topologies. The results show that the KeySet outperforms the KeyFlow scheme [1] by at least 25% in terms of the length of the forwarding label. Moreover, we show that KeySet is very efficient when applied to fat-trees.
In addition to operating the imaging ellipsometric measurements by four-specific temporal phases in the photoelastic modulated ellipsometry, we added the fifth one to solve the initial phase of the photoelastic modulator. This methodology has been developed to conquer the slow imaging processing of charge-coupled device camera for the stroboscopic illumination in the polarization modulated imaging ellipsometry. Without any calibration in its initial phase, we can perform the ellipsometric measurement by the measurements of intensity at five-specific temporal phases. The intensities of a full cycle for a point on SiO(2)∕Si thin film were measured and analyzed for verifying this algorithm. The five stroboscopic illuminations were performed to measure the two-dimensional distribution of the same SiO(2)∕Si thin film.
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