Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving ...
Abstract: Deep neural networks have demonstrated highly competitive performance in super-resolution (SR) for natural images by learning mappings from low-resolution (LR) to high-resolution (HR) images ...
Detecting concealed explosives and chemical threats constitutes a critical challenge in global security, yet current ...
Monitoring forest health typically relies on remote sensing tools such as light detection and ranging (LiDAR), radar, and multispectral photography. While radar and LiDAR penetrate canopies to reveal ...
Abstract: Hyperspectral single-image super-resolution (SISR) remains a challenging task due to the difficulty of restoring fine spatial details and preserving spectral fidelity across a wide range of ...
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