Low Light HSI

Low-light hyperspectral imaging (HSI) focuses on improving the quality and usability of hyperspectral data acquired under poor lighting conditions, addressing challenges like low visibility and spectral distortion. Current research emphasizes developing advanced image enhancement techniques, often employing deep learning architectures such as those incorporating Laplacian pyramid decomposition and attention mechanisms to recover spatial and spectral details. These advancements are crucial for expanding the applicability of HSI in various fields, including remote sensing and medical imaging, where low-light conditions are frequently encountered, ultimately improving the accuracy and reliability of analyses based on this data.