Sparse Reconstruction of Compressed Sensing Multi-spectral Data using Cross-Spectral Multi-layered Conditional Random Field Model

The broadband spectrum contains more information than what the human eye can detect. Spectral information from different wavelengths can provide unique information about the intrinsic properties of an object. Recently compressed sensing imaging systems with low acquisition time have been introduced. To utilize compressed sensing strategies, strong reconstruction algorithms that can reconstruct a signal from sparse observations are required. This work proposes a Cross-Spectral Multi-layerd Conditional Random Field(CS-MCRF) approach for sparse reconstruction of multi-spectral compressive sensing data in multi-spectral stereoscopic vision imaging systems. The CS-MCRF will use information between neighboring spectral bands to better utilize available information for reconstruction. This method was evaluated using simulated compressed sensing multi-spectral imaging data. Results show improvement over existing techniques in preserving spectral fidelity while effectively inferring missing information from sparsely available observations.