Biomedical Imaging GroupSTI
English only   BIG > Publications > Neural Networks

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

Convolutional Neural Networks for Inverse Problems in Imaging—A Review

M.T. McCann, K.H. Jin, M. Unser

IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 85-95, November 2017.

In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, superresolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions:

  • From where do the training data come?
  • What is the architecture of the CNN?
  • How is the learning problem formulated and solved?

We also mention a few key theoretical papers that offer perspectives on why CNNs are appropriate for inverse problems, and we point to some next steps in the field.

AUTHOR="McCann, M.T. and Jin, K.H. and Unser, M.",
TITLE="Convolutional Neural Networks for Inverse Problems in Imaging---A
JOURNAL="{IEEE} Signal Processing Magazine",

© 2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.