BlindNet: an untrained learning approach toward computational imaging with model uncertainty
Xiangyu Zhang, Fei Wang, Guohai Situ
Abstract
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.
Topics & Concepts
Computer scienceTask (project management)Artificial intelligenceInverse problemMachine learningQuality (philosophy)Computational modelObject (grammar)EngineeringMathematicsSystems engineeringPhilosophyEpistemologyMathematical analysisAdvanced X-ray Imaging TechniquesImage Processing Techniques and ApplicationsAdvanced Electron Microscopy Techniques and Applications