Equivariant Imaging

Equivariant imaging (EI) is a self-supervised learning framework leveraging the inherent symmetries in image data to reconstruct images from incomplete or noisy measurements, overcoming the limitations of traditional supervised methods that require extensive ground truth data. Current research focuses on applying EI to diverse imaging modalities, including hyperspectral, multispectral, and single-photon imaging, often employing diffusion models and other deep learning architectures for improved reconstruction accuracy. This approach holds significant promise for advancing various fields, from medical imaging and remote sensing to computational microscopy, by enabling high-quality image reconstruction even with limited or imperfect data.