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Speckle Noise Removal Based on Adaptive Total Variation Model

  • Bo ChenEmail author
  • Jinbin Zou
  • Wensheng Chen
  • Xiangjun Kong
  • Jianhua Ma
  • Feng Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11256)

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

Image denoising Speckle noise Total variation Diffusion properties 

Notes

Acknowledgement

This paper is partially supported by the Natural Science Foundation of Guangdong Province (2018A030313364), the Science and Technology Planning Project of Shenzhen City (JCYJ20140828163633997), the Natural Science Foundation of Shenzhen (JCYJ20170818091621856) and the China Scholarship Council Project (201508440370).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Bo Chen
    • 1
    • 2
    Email author
  • Jinbin Zou
    • 1
  • Wensheng Chen
    • 1
    • 2
  • Xiangjun Kong
    • 3
  • Jianhua Ma
    • 4
  • Feng Li
    • 5
  1. 1.Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and StatisticsShenzhen UniversityShenzhenChina
  2. 2.Shenzhen Key Laboratory of Media SecurityShenzhen UniversityShenzhenChina
  3. 3.School of Mathematical SciencesQufu Normal UniversityQufuChina
  4. 4.Department of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina
  5. 5.China Ship Scientific Research CenterWuxiChina

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