Deep Learning Techniques for Inverse Problems in Imaging

Ongie, Greg; Jalal, Ajil; Metzler, Christopher A.; Baraniuk, Richard G.; Dimakis, Alexandros G.; Willett, Rebecca

doi:10.1109/jsait.2020.2991563

articleIEEE Journal on Selected Areas in Information TheoryMay 1, 2020Closed access

Deep Learning Techniques for Inverse Problems in Imaging

GOGreg Ongie AJAjil Jalal CAChristopher A. Metzler RGRichard G. Baraniuk AGAlexandros G. Dimakis

University of Chicago · The University of Texas at Austin · +2 more institutions

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Abstract

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the tradeoffs associated with these different reconstruction…

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Authors

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Topics & keywords

Topics

Keywords

Categorization
Artificial intelligence
Computer science
Machine learning
Taxonomy (biology)
Variety (cybernetics)
Deep learning
Deep neural networks

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