Multispectral sensing is specifically designed to provide quantitative spectral information about various materials or scenes. Using spectral information, various properties of objects can be measured and analysed. Microscopy, the observing and imaging of objects at the micron- or nano-scale, is one application where multispectral sensing can be advantageous, as many fields of science and research that use microscopy would benefit from observing a specimen in multiple wavelengths. Multispectral microscopy is available, but often requires the operator of the device to switch filters which is a labor intensive process. Furthermore, the need for filter switching makes such systems particularly limiting in cases where the sample contains live species that are constantly moving or exhibit transient phenomena. Direct methods for capturing multispectral data of a live sample simultaneously can also be challenging for microscopy applications as it requires an elaborate optical systems design which uses beamsplitters and a number of detectors proportional to the number of bands sought after. Such devices can therefore be quite costly to build and difficult to maintain, particularly for microscopy. In this paper, we present the concept of virtual spectral demultiplexing imaging (VSDI) microscopy for low-cost in-situ multispectral microscopy of transient phenomena. In VSDI microscopy, the spectral response of a color detector in the microscope is characterized and virtual spectral demultiplexing is performed on the simultaneously-acquired broadband detector measurements based on the developed spectral characterization model to produce microscopic imagery at multiple wavelengths. The proposed VSDI microscope was used to observe colorful nanowire arrays at various wavelengths simultaneously to illustrate its efficacy.
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-layered 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.