Review of Advancements in Hyperspectral Imaging for Low-Light Environments
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2024-10-14
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Abstract
Advancements in low-cost hyperspectral imaging for low-light environments represent a significant leap forward in imaging technology, blending spectroscopy with advanced imaging capabilities to provide detailed analyses of material composition and characteristics. Traditionally, hyperspectral imaging has been an expensive and complex field, primarily utilized in remote sensing, environmental monitoring, and medical imaging. However, recent innovations have focused on reducing costs and enhancing accessibility, making it feasible for a broader range of applications, including portable devices like smartphones, UAVs, and DIY systems. Significant advancements include the development of on-chip computational hyperspectral imaging frameworks, which integrate broadband filtering materials directly onto imaging sensors, and hyperspectral lightfield cameras that combine hyperspectral and 3D analysis capabilities. These technologies improve light throughput and spatial-temporal resolution, thus enabling high-quality imaging even with lower-cost components. Quanta image sensors (QIS) have further enhanced low-light performance by utilizing high-speed readout and photon-number-resolving capabilities, proving particularly useful in medical diagnostics and environmental monitoring. Applications of these advancements are vast and varied. In agriculture, hyperspectral imaging facilitates precision farming by detecting crop stress and optimizing yield. In environmental monitoring, it allows for the detailed observation of vegetation health and water quality. The technology also plays a critical role in food quality control and biomedical imaging, where it provides non-destructive methods to assess food safety and diagnose diseases at an early stage. Despite these promising developments, several challenges remain. The complexity and cost of hyperspectral systems, along with the need for advanced data processing and storage capabilities, are significant hurdles. Issues such as spectral and spatial resolution, noise sensitivity, and data management continue to require sophisticated solutions and further research. Nonetheless, the ongoing advancements in sensor design, algorithm development, and machine learning integration hold promise for overcoming these challenges and expanding the scope and utility of hyperspectral imaging in various fields.