Deep learning with physical and power-spectral priors for robust image inversion

Computational imaging is the class of imaging systems that utilizes inverse algorithms to recover unknown objects of interest from physical measurements. Deep learning has been used in computational imaging, typically in the supervised mode and in an End-to-End fashion. However, treating the machine learning algorithm as a mere black-box is not the most efficient, as the measurement formation process (a.k.a. the forward operator), which depends on the optical apparatus, is known to us. Therefore, it is inefficient to let the neural network to explain, at least partly, the system physics. Also, some prior knowledge of the class of objects of interest can be leveraged to make the training more efficient. The main theme of this thesis is to design more efficient deep learning algorithms with the help of physical and power-spectral priors.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Deng, Mo,Ph. D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• George Barbastathis.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127013

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127013