Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements
| Autor: | Dongdong Chen, Julian Tachella, Mike E. Davies |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Self- & semi- & meta- & unsupervised learning Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Physics-based vision and shape-from-X Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing Low-level vision Computational photography Medical FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Optimization methods biological and cell microscopy |
| Zdroj: | Chen, D, Tachella, J & Davies, M E 2022, Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements . in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5637-5646, IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022, New Orleans, Louisiana, United States, 19/06/22 . https://doi.org/10.1109/CVPR52688.2022.00556 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| DOI: | 10.1109/CVPR52688.2022.00556 |
| Popis: | Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. However, most existing networks are trained with clean signals which are often hard or impossible to obtain. Equivariant imaging (EI) is a recent self-supervised learning framework that exploits the group invariance present in signal distributions to learn a reconstruction function from partial measurement data alone. While EI results are impressive, its performance degrades with increasing noise. In this paper, we propose a Robust Equivariant Imaging (REI) framework which can learn to image from noisy partial measurements alone. The proposed method uses Stein's Unbiased Risk Estimator (SURE) to obtain a fully unsupervised training loss that is robust to noise. We show that REI leads to considerable performance gains on linear and nonlinear inverse problems, thereby paving the way for robust unsupervised imaging with deep networks. Code is available at: https://github.com/edongdongchen/REI. CVPR 2022. Code: https://github.com/edongdongchen/REI |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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