Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations-Reference-Cited by-同舟云学术

Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations

Published:2019 Issue: Volume: Page:549-557
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Hariharan Sai Gokul^ORCID,Kaethner Christian^ORCID,Strobel Norbert^ORCID,Kowarschik Markus^ORCID,Albarqouni Shadi^ORCID,Fahrig Rebecca^ORCID,Navab Nassir^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-32226-7_61

Reference13 articles.

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2. Cerciello, T., Bifulco, P., Cesarelli, M., Fratini, A.: A comparison of denoising methods for x-ray fluoroscopic images. Biomed. Signal Process. Control 7(6), 550–559 (2012)

3. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

4. Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862–2869 (2014)

5. Hariharan, S.G., et al.: Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system. Int. J. Comput. Assist. Radiol. Surg. 14(7), 1117–1126 (2019)

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