Advances in Deep-learning-based Inverse Problems (DLB IP)

Posted 2020-12-16 mlsquare

 

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Announcements

- This repository provides references to recent advances in deep-learning-based inverse problems (DLB IP), and will be updated once every month with the hope of expediting the development of this field.

- The main content of this repository consists of 2 components: (0) tutorials and reviews; (1) empirical advances section which contains references to empirical investigations of inverse problems such as unrolling iterative algorithms as deep neural networks; (2) theoretical advances section which contains references to theoretical studies of GANs such as theoretical performance guarantees of reconstruction.

- This repository won‘t be possible without the efforts from many contributors who are listed in the end. If you want to contribute to this repository, you can simply put the reference information in the comment for this repository or send us an email. Please follow the following formats to help us: (1) send emails to [e-mail] ; (2) set the email title as "Refrences_DLB IP_Institute"; (3) set the references format as Vancouver (available in Google Scholar) with hyperlinks to the reference and its implementation (if it‘s available), i.e.,

Mousavi A, Patel AB, Baraniuk RG. A deep learning approach to structured signal recovery. In2015 53rd annual allerton conference on communication, control, and computing (Allerton) 2015 Sep 29 (pp. 1336-1343). IEEE. Github

 

- If you have any constructive suggestions, please leave them as comments to this repository.

 

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Tutorials and Reviews

- Ongie G, Jalal A, Baraniuk CA, Dimakis AG, Willett R. Deep learning techniques for inverse problems in imaging. IEEE Journal on Selected Areas in Information Theory. 2020 May 1.

- Bustin A, Fuin N, Botnar RM, Prieto C. From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction. Frontiers in Cardiovascular Medicine. 2020 Feb 25;7:17.

- Zhang HM, Dong B. A review on deep learning in medical image reconstruction. Journal of the Operations Research Society of China. 2020 Jan 10:1-30.

- Lucas A, Iliadis M, Molina R, Katsaggelos AK. Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Processing Magazine. 2018 Jan 10;35(1):20-36.

- McCann MT, Jin KH, Unser M. A review of convolutional neural networks for inverse problems in imaging. arXiv preprint arXiv:1710.04011. 2017 Oct 11.

 

To Be Added

 

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Empirical Advances

 

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Theoretical Advances

 

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Contributors

This repository will be impossible without the contributions from the following:

* UserID, Affiliation, contributing since, number of reference contribution

 

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References

 

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