An experimental investigation of super-resolution imaging from measurements of projections onto a random
basis is presented. In particular, a laboratory imaging system was constructed following an architecture that
has become familiar from the theory of compressive sensing. The system uses a digital micromirror array
located at an intermediate image plane to introduce binary matrices that represent members of a basis set.
The system model was developed from experimentally acquired calibration data which characterizes the system
output corresponding to each individual mirror in the array. Images are reconstructed at a resolution limited
by that of the micromirror array using the split Bregman approach to total-variation regularized optimization.
System performance is evaluated qualitatively as a function of the size of the basis set, or equivalently, the
number of snapshots applied in the reconstruction.
The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest
burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires
fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy
of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms
for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These
algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models
for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD)
[Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical
random sensing model closer to CS theory, but not necessarily implementable with existing technology. We
simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes
- an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models,
we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The
results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements
than standard hyperspectral cameras.
Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spatial and spectral dimensions. We utilize a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) CS model to simulate CS measurements from HyMap images. Flake et al's novel reconstruction algorithm, which combines a spectral smoothing parameter and spatial total variation (TV), is used to create high resolution hyperspectral imagery.1 We examine the e ect of the number of measurements, which corresponds to the percentage of physical data sampled, on the delity of simulated data. The impacts of the CS sensor model and reconstruction of the data cloud and the utility for various hyperspectral applications are described to identify the strengths and limitations of CS.