Compressive Spectral Imaging

Spectral-spatial classification from multi-sensor compressive measurements using superpixels.

In this work, we propose to obtain features by considering the spectral information extracted from Hyperspectral CSI measurements, and the local spatial information extracted by clustering the Multispectral CSI measurements.

Adaptive grayscale compressive spectral imaging using optimal blue noise coding patterns

This paper proposes an adaptive grayscale coded aperture design which combines the advantages of blue noise and block-unblock coding patterns to provide high-quality image reconstructions and redundancy in the sampling.

Coded aperture design for compressive spectral subspace clustering

This paper proposes to design a set of coding patterns such that inter-class and intra-class data structure is preserved after the CSI acquisition in order to improve clustering results directly on the compressed domain. To validate the coding pattern design, an algorithm based on sparse subspace clustering (SSC) is proposed to perform clustering on the compressed measurements.

Multiple snapshot colored compressive spectral imager

This paper extends the concept of SCCSI to a system admitting multiple snapshot acquisition by rotating the dispersive element, so the dispersed spatio-spectral source is coded and integrated at different detector pixels in each rotation. Thus, a different set of coded projections is captured using the same optical components of the original architecture.