Summary
Modern imaging technologies are central to progress in science, medicine, and engineering. Yet, many advanced imaging systems operate under physical or resource limitations that make it difficult to directly acquire high-quality images. Computational imaging addresses these challenges by using algorithms to reconstruct images from incomplete or indirect measurements. In recent years, deep learning has enabled new capabilities in computational imaging, but current methods assume that the training and test data share the same conditions. This assumption often does not hold in real-world settings
