| Paper ID | SS-2.3 |
| Paper Title |
Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework |
| Authors |
Wei Chen, Beijing Jiaotong University, China; David Wipf, Amazon AI Research Lab, China; Miguel R.D. Rodrigues, University College London, United Kingdom |
| Session | SS-2: Deep Learning Methods for Solving Linear Inverse Problems |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Deep Learning Methods for Solving Linear Inverse Problems |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
