Unpaired image denoising using a generative adversarial network in X-ray CT
Unpaired image denoising using a generative adversarial network in X-ray CT
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data. The proposed method uses a fidelity-embedded generative adversarial network (GAN) to learn a denoising function from unpaired training data of low-dose CT (LDCT) and standard-dose CT (SDCT) images...
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Full title
Unpaired image denoising using a generative adversarial network in X-ray CT
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TN_cdi_proquest_journals_2193414025
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2193414025
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E-ISSN
2331-8422
DOI
10.48550/arxiv.1903.06257
