Photon Efficient Imaging

Photon-efficient imaging focuses on extracting high-quality images from scenes with extremely limited light, aiming to minimize the number of photons needed for accurate reconstruction. Current research emphasizes computational methods, including advanced regularization techniques (like total variation and ℓp-norms) within statistical models (Poisson and negative binomial) to handle noise and sparsity in low-photon data, and deep learning architectures (e.g., convolutional neural networks and hypernetworks) to optimize both image reconstruction and computational efficiency. These advancements are crucial for improving the performance of various imaging systems in challenging environments, such as biomedical imaging, astronomy, and autonomous driving, where photon scarcity is a significant limitation.