English

Noise-resilient approach for deep tomographic imaging

Image and Video Processing 2022-11-29 v1 Computer Vision and Pattern Recognition
Authors: Zhen Guo , Zhiguang Liu , Qihang Zhang , George Barbastathis , Michael E. Glinsky
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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.

Keywords

image denoising deep learning for image reconstruction photoacoustic imaging

Cite

@article{arxiv.2211.15456,
  title  = {Noise-resilient approach for deep tomographic imaging},
  author = {Zhen Guo and Zhiguang Liu and Qihang Zhang and George Barbastathis and Michael E. Glinsky},
  journal= {arXiv preprint arXiv:2211.15456},
  year   = {2022}
}

Comments

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

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