Noise-resilient approach for deep tomographic imaging

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Nov 22, 2022
Zhen Guo, Zhiguang Liu, Qihang Zhang, George Barbastathis, Michael E. Glinsky
Figure 1 for Noise-resilient approach for deep tomographic imaging

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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.

* 2022 CLEO (the Conference on Lasers and Electro-Optics) conference submission  
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