TY - GEN
T1 - On the Convex Model of Speckle Reduction
AU - Fang, Faming
AU - Fang, Yingying
AU - Zeng, Tieyong
N1 - Funding Information:
Acknowledgements The authors would like to sincerely thank the reviewers for their valuable and constructive comments. This work is sponsored by “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, the key project of the National Natural Science Foundation of China (No. 61731009), the National Science Foundation of China (11271049, 61501188), RGC 12302714, and the Direct Grant for Research of the Chinese University of Hong Kong.
PY - 2018/11/19
Y1 - 2018/11/19
N2 - Speckle reduction is an important issue in image processing realm. In this paper, we propose a novel model for restoring degraded images with multiplicative noise which follows a Nakagami distribution. A general penalty term based on the statistical property of the speckle noise is used to guarantee the convexity of the denoising model. Moreover, to deal with the minimizing problem, a generalized Bermudez-Moreno algorithm is adopted and its convergence is analysed. The experimental results on some images subject to multiplicative noise as well as comparisons to other state-of-the-art methods are also presented. The results can verify that the new model is reasonable.
AB - Speckle reduction is an important issue in image processing realm. In this paper, we propose a novel model for restoring degraded images with multiplicative noise which follows a Nakagami distribution. A general penalty term based on the statistical property of the speckle noise is used to guarantee the convexity of the denoising model. Moreover, to deal with the minimizing problem, a generalized Bermudez-Moreno algorithm is adopted and its convergence is analysed. The experimental results on some images subject to multiplicative noise as well as comparisons to other state-of-the-art methods are also presented. The results can verify that the new model is reasonable.
UR - http://www.scopus.com/inward/record.url?scp=85057479792&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91274-5_6
DO - 10.1007/978-3-319-91274-5_6
M3 - Conference proceeding
AN - SCOPUS:85057479792
SN - 9783319912738
T3 - Mathematics and Visualization
SP - 121
EP - 141
BT - Imaging, Vision and Learning Based on Optimization and PDEs
A2 - Tai, Xue-Cheng
A2 - Bae, Egil
A2 - Lysaker, Marius
PB - Springer Cham
T2 - International conference on Imaging, Vision and Learning Based on Optimization and PDEs, IVLOPDE 2016
Y2 - 29 August 2016 through 2 September 2016
ER -