Computational imaging is a rapidly grow ing area that seeks to enhance the capabilities of imaging instruments by viewing imaging as an inverse problem. There are currently two distinct ap proaches for designing computational imaging methods: model-based and lear ning-based. Model-based methods leverage analytical signal properties and often come with theoretical guarantees and insights. Learning-based method s leverage data-driven representations for best empirical performance thro ugh training on large datasets. This talk presents Regularization by Artif act Removal (RARE)\, as a framework for reconciling both viewpoints by pro viding a learning-based extension to the classical theory. RARE relies on pre-trained “artifact-removing deep neural nets” for infusing learned prior knowledge into an inverse problem\, while maintaining a clear separa tion between the prior and physics-based acquisition model. Our results indi cate that RARE can achieve state-of-the-art performance in different compu tational imaging tasks\, while also being amenable to rigorous theoretical analysis. We will focus on the applications of RARE in biomedical imaging \, including magnetic resonance and tomographic imaging.
\n\nThis talk will be based on the following references
\n\n\;
\n LOCATION:https://researchseminars.org/talk/MPML/46/ END:VEVENT END:VCALENDAR