Hyperspectral Image Reconstruction

Hyperspectral image reconstruction aims to recover high-quality, full spectral information from incomplete or compressed measurements, often acquired using cost-effective snapshot imaging systems. Current research heavily utilizes deep learning, employing architectures like transformers and convolutional neural networks (CNNs), often within unfolding frameworks that incorporate adaptive step sizes and attention mechanisms to leverage spatial and spectral correlations. These advancements are crucial for expanding the applications of hyperspectral imaging across diverse fields, including medical imaging, agriculture, and remote sensing, by making the technology more accessible and efficient.