TY - JOUR

T1 - Using Deep Neural Networks for Inverse Problems in Imaging

T2 - Beyond Analytical Methods

AU - Lucas, Alice

AU - Iliadis, Michael

AU - Molina, Rafael

AU - Katsaggelos, Aggelos K.

N1 - Funding Information:
This work was supported in part by the U.S. Department of Energy under grant DE-NA0002520, Office of Naval Research award N00014-15-1-2735, National Science Foundation IDEAS program, the Defense Advanced Research Projects Agency ReImagine, and the Spanish Ministry of Economy and Competitiveness through projects TIN2013-43880-R and DPI2016-77869-C2-2-R.
Publisher Copyright:
© 2017 IEEE.

PY - 2018/1

Y1 - 2018/1

N2 - Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches. Unlike analytical methods for which the problem is explicitly defined and domain-knowledge carefully engineered into the solution, deep neural networks (DNNs) do not benefit from such prior knowledge and instead make use of large data sets to learn the unknown solution to the inverse problem. In this article, we review deep-learning techniques for solving such inverse problems in imaging. More specifically, we review the popular neural network architectures used for imaging tasks, offering some insight as to how these deep-learning tools can solve the inverse problem. Furthermore, we address some fundamental questions, such as how deeplearning and analytical methods can be combined to provide better solutions to the inverse problem in addition to providing a discussion on the current limitations and future directions of the use of deep learning for solving inverse problem in imaging.

AB - Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches. Unlike analytical methods for which the problem is explicitly defined and domain-knowledge carefully engineered into the solution, deep neural networks (DNNs) do not benefit from such prior knowledge and instead make use of large data sets to learn the unknown solution to the inverse problem. In this article, we review deep-learning techniques for solving such inverse problems in imaging. More specifically, we review the popular neural network architectures used for imaging tasks, offering some insight as to how these deep-learning tools can solve the inverse problem. Furthermore, we address some fundamental questions, such as how deeplearning and analytical methods can be combined to provide better solutions to the inverse problem in addition to providing a discussion on the current limitations and future directions of the use of deep learning for solving inverse problem in imaging.

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U2 - 10.1109/MSP.2017.2760358

DO - 10.1109/MSP.2017.2760358

M3 - Article

AN - SCOPUS:85040644946

VL - 35

SP - 20

EP - 36

JO - IEEE Audio and Electroacoustics Newsletter

JF - IEEE Audio and Electroacoustics Newsletter

SN - 1053-5888

IS - 1

M1 - 8253590

ER -