Adaptive denoising for magnetic resonance image based on nonlocal structural similarity and low-rank sparse representation-Reference-Cited by-同舟云学术

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Adaptive denoising for magnetic resonance image based on nonlocal structural similarity and low-rank sparse representation

Published:2022-10-26 Issue: Volume: Page:
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Wang Hongyu,Li Ying,Ding Songtao,Pan Xiaoying,Gao Zhanyi,Wan Shaohua,Feng Jun

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

https://link.springer.com/content/pdf/10.1007/s10586-022-03773-2.pdf

Reference35 articles.

1. Chen, Z., Fu, Y., Xiang, Y., Zhu, Y.: A novel MR image denoising via LRMA and NLSS. Signal Process. 185, 108109 (2021)

2. Wang, H., Zhang, D., Ding, S., Gao, Z., Feng, J., Wan, S.: Rib segmentation algorithm for x-ray image based on unpaired sample augmentation and multi-scale network. Neural Comput. Appl. 1–15 (2021)

3. Chen, G., Zhang, P., Wu, Y., Shen, D., Yap, P.-T.: Denoising magnetic resonance images using collaborative non-local means. Neurocomputing 177, 215–227 (2016)

4. Ni, B., Liu, Z., Cai, X., Nappi, M., Wan, S.: Segmentation of ultrasound image sequences by combing a novel deep Siamese network with a deformable contour model. Neural Comput. Appl. 1–15 (2022)

5. Ding, S., Wang, H., Lu, H., Nappi, M., Wan, S.: Two path gland segmentation algorithm of colon pathological image based on local semantic guidance. IEEE J. Biomed. Health Inform. (2022). https://doi.org/10.1109/JBHI.2022.3207874

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