Chen, Wei;
Wipf, David;
Rodrigues, Miguel;
(2021)
Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework.
In:
2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
(pp. pp. 8098-8102).
IEEE
Preview |
PDF
ICASSP.pdf - Accepted Version Download (525kB) | Preview |
Abstract
Linear inverse problems appear in many applications, where different algorithms are typically employed to solve each inverse problem. Nowadays, the rapid development of deep learning (DL) provides a fresh perspective for solving the linear inverse problem: a number of well-designed network architectures results in state-of-the-art performance in many applications. In this overview paper, we present the combination of the DL and the Plug-and-Play priors (PPP) framework, showcasing how it allows solving various inverse problems by leveraging the impressive capabilities of existing DL based denoising algorithms. Open challenges and potential future directions along this line of research are also discussed.
| Type: | Proceedings paper |
|---|---|
| Title: | Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework |
| Event: | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Location: | ELECTR NETWORK |
| Dates: | 6 Jun 2021 - 11 Jun 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICASSP39728.2021.9413947 |
| Publisher version: | https://doi.org/10.1109/ICASSP39728.2021.9413947 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Acoustics, Computer Science, Computer Science, Artificial Intelligence, Computer Science, Software Engineering, Deep learning, Engineering, Engineering, Electrical & Electronic, Imaging Science & Photographic Technology, linear inverse problems, NETWORKS, plug-and-play priors, Science & Technology, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172104 |
Archive Staff Only
 |
View Item |
