Over the last decade, hyperspectral imaging has attracted considerable interest in civil, environmental, aerial, military, and biological applications that require the estimation of physical parameters from complex surfaces and the identification via remote sensing of complex materials having fine spectral signatures. Despite these significant advances, hyperspectral imaging still requires high setup costs, is affected by a slow speed of data processing, and necessitates the use of substantial amounts of computational resources to post-process the large data generated. This project addresses the issues mentioned above by implementing a new concept of hyperspectral imaging based on integrated flat-optics and delivers a new class of low-cost and simple to setup hyperspectral cameras in optoelectronic hardware (HOCULUS system), which does not require the use of spectral analyzers or complex mechanical filters. HOCULUS system can integrate hyperspectral functionalities for pattern recognition, semantic image segmentation, and label-free classification in inexpensive hardware that can retrieve projector's barcodes at camera speed and high resolutions, opening up the possibility for real-time acquisition and processing hyperspectral videos. These results could significantly accelerate different research lines in computer vision, especially in bio-imaging, where they may enable a novel understanding of complex dynamical processes in multicellular organisms and the fast identification of diseases at the point of care.
Investigator:
- Andrea Fratalocchi
- Maksim Makarenko