High Speed Imaging

High-speed imaging aims to capture rapidly changing events with high fidelity, overcoming limitations of traditional cameras in speed and light sensitivity. Current research focuses on developing novel sensor technologies, such as event cameras and SPADs, and advanced computational methods, including deep learning architectures (e.g., convolutional neural networks, residual networks) and physics-informed neural networks, to improve image reconstruction speed and quality from often undersampled or noisy data. These advancements are significantly impacting diverse fields, enabling faster and more accurate imaging in applications ranging from medical imaging and industrial inspection to astronomy and robotics.