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Noise-resilient approach for deep tomographic imaging
November 22, 2022 Β· Declared Dead Β· π Conference on Lasers and Electro-Optics
"No code URL or promise found in abstract"
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Authors
Zhen Guo, Zhiguang Liu, Qihang Zhang, George Barbastathis, Michael E. Glinsky
arXiv ID
2211.15456
Category
eess.IV: Image & Video Processing
Cross-listed
cs.CV
Citations
1
Venue
Conference on Lasers and Electro-Optics
Last Checked
4 months ago
Abstract
We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantages of our framework may further enable low-photon tomographic imaging.
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