A new non-convex sparse optimization method for image restoration
Author:
Funder
Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02611-1.pdf
Reference24 articles.
1. Tatsugami, F., Higaki, T., Nakamura, Y., et al.: Deep learning-based image restoration algorithm for coronary CT angiography. Eur. Radiol. 29(10), 5322–5329 (2019)
2. Wang, Z., Zhang, Z., Dong, L., et al.: Jitter detection and image restoration based on generative adversarial networks in satellite images. Sensors 21(14), 4693 (2021)
3. Li, C., Li, J., Luo, Z.: An impulse noise removal model algorithm based on logarithmic image prior for medical image. Signal Image Video Process. 15, 1145–1152 (2021)
4. Shi, Y., Zhang, X., Wang, M., et al.: An adaptive non-convex hybrid total variation regularization method for image reconstruction in electrical impedance tomography. Flow Meas. Instrum. 79, 101937 (2021)
5. Tikhonov, A.N., Arsenn, V.Y.: Solution of Ill-Posed Problem. Winston and Sons, Washington (1977)
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