Advanced microscopy techniques, including three-dimensional, super-resolution and quantitative phase microscopy remain at the forefront of biomedical discovery. These methods enable researchers to visualize complex molecular processes and interactions at the level of single molecules or molecular complexes, capturing yet unseen information and pushing the boundaries of our understanding of health and disease.

These innovations have been made possible, among others, through rapid progress in biophotonics, as well as computational processing and analysis of image-based data. However, advanced biophotonics comes at the cost of complex equipment, as well as difficult and lengthy data acquisition and necessitates highly-trained personnel. Artur and his team demonstrated in several works that this hurdle can be addressed using generative and discriminative AI algorithms by formulating the conversion from conventional microscopy modalities like widefield to advanced like super-resolution as a set of inverse problems.

Hereby, better performance in these algorithms can be achieved by incorporating nuance of the data domain into the algorithm design, as well as leveraging synthetic data pre-training.