BlindNet: an untrained learning approach toward computational imaging with model uncertainty

The solution of an inverse problem in computational imaging (CI) often requires the knowledge of the physical model and/or the object. However, in many practical applications, the physical model may not be accurately characterized, leading to model uncertainty that affects the quality of the reconstructed image. Here, we propose a novel untrained learning approach towards CI with model uncertainty, and demonstrate it in phase retrieval, an important CI task that is widely encountered in biomedical imaging and industrial inspection.

Automated summary

The solution of an inverse problem in computational imaging often relies on accurate knowledge of the physical model and object, but model uncertainty in practical applications can degrade the quality of reconstructed images. In this paper, we propose a novel untrained learning approach to address computational imaging with model uncertainty, demonstrated through phase retrieval, an important task in biomedical imaging and industrial inspection.