@inproceedings{754d1eb62fda4b24992ca3cb76408ac1,
title = "Speckle noise removal based on adaptive total variation model",
abstract = "For removing the speckle noise in ultrasound images, researchers have proposed many models based on energy minimization methods. At the same time, traditional models have some disadvantages, such as, the low speed of energy diffusion which can not preserve the sharp edges. In order to overcome those disadvantages, we introduce an adaptive total variation model to deal with speckle noise in ultrasound image for retaining the fine detail effectively and enhancing the speed of energy diffusion. Firstly, a new convex function is employed as regularization term in the adaptive total variation model. Secondly, the diffusion properties of the new model are analyzed through the physical characteristics of local coordinates. The new energy model has different diffusion velocities in different gradient regions. Numerical experimental results show that the proposed model for speckle noise removal is superior to traditional models, not only in visual effect, but also in quantitative measures.",
keywords = "Diffusion properties, Image denoising, Speckle noise, Total variation",
author = "Bo Chen and Jinbin Zou and Wensheng Chen and Xiangjun Kong and Jianhua Ma and Feng Li",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018 ; Conference date: 23-11-2018 Through 26-11-2018",
year = "2018",
doi = "10.1007/978-3-030-03398-9\_17",
language = "英语",
isbn = "9783030033972",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "191--202",
editor = "Jian-Huang Lai and Hongbin Zha and Jie Zhou and Cheng-Lin Liu and Tieniu Tan and Nanning Zheng and Xilin Chen",
booktitle = "Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings",
}