Abstract
End-to-end deep neural networks (DNNs) have become state-of-the-art (SOTA) for solving inverse problems. Despite their outstanding performance, during deployment, such networks are sensitive to minor variations in the training pipeline and often fail to reconstruct small but important details, a feature critical in medical imaging, astronomy, or defence. Such instabilities in DNNs can be explained by the fact that they ignore the forward measurement model during deployment, and thus fail to enforce consistency between their output and the input measurements. To overcome this, we propose a framework that transforms any DNN for inverse problems into a measurement-consistent one. This is done by appending to it an implicit layer (or deep equilibrium network) designed to solve a model-based optimization problem. The implicit layer consists of a shallow learnable network that can be integrated into the end-to-end training. Experiments on single-image super-resolution show that the proposed framework leads to significant improvements in reconstruction quality and robustness over the SOTA DNNs.
Original language | English |
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Title of host publication | 2023 IEEE International Conference on Acoustics, Speech and Signal Processing |
Publisher | IEEE |
ISBN (Electronic) | 9781728163277 |
DOIs | |
Publication status | Published - 5 May 2023 |
Event | 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023 - Rhodes Island, Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 Conference number: 48 https://2023.ieeeicassp.org/ https://2023.ieeeicassp.org |
Conference
Conference | 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023 |
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Abbreviated title | ICASSP 2023 |
Country/Territory | Greece |
City | Rhodes Island |
Period | 4/06/23 → 10/06/23 |
Internet address |
Keywords
- ADMM
- Image super-resolution
- deep equilibrium networks
- fixed-point iterations
- implicit layers
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering