UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework

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 Green open access

[thumbnail of ICASSP.pdf]
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
Downloads since deposit
73Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item