Noise-resilient approach for deep tomographic imaging
arXiv:2211.15456
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.
2022 CLEO (the Conference on Lasers and Electro-Optics) conference submission