Detail-aware image denoising via structure preserved network and residual diffusion model-Reference-Cited by-同舟云学术

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Detail-aware image denoising via structure preserved network and residual diffusion model

Published:2024-04-18 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Wu Jing,Wu Hao^ORCID,Yuan Guowu

Funder

the National Natural Science Foundation of China

the Yunnan Provincial Department of Science and Technology-Yunnan University Joint Special Project for Double-Class Construction

the Practical Innovation Fund Project for Professional Degree Graduate Students of Yunnan University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00371-024-03353-y.pdf

Reference67 articles.

1. 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). https://doi.org/10.1109/TIP.2007.901238

2. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2272–2279 (2009). https://doi.org/10.1109/ICCV.2009.5459452

3. Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2012). https://doi.org/10.1109/TIP.2012.2235847

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

5. Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2774–2781 (2014).

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