Numerical Algorithms

, Volume 78, Issue 2, pp 513–533 | Cite as

Speckle noise removal in ultrasound images by first- and second-order total variation

  • Si Wang
  • Ting-Zhu Huang
  • Xi-Le Zhao
  • Jin-Jin Mei
  • Jie Huang
Original Paper


Speckle noise contamination is a common issue in ultrasound imaging system. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized for speckle noise removal. However, TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant. In this paper, we propose a new model to overcome this deficiency. In this model, the regularization term is represented by a combination of total variation and high-order total variation, while the data fidelity term is depicted by a generalized Kullback-Leibler divergence. The proposed model can be efficiently solved by alternating direction method with multipliers (ADMM). Compared with some state-of-the-art methods, the proposed method achieves higher quality in terms of the peak signal to noise ratio (PSNR) and the structural similarity index (SSIM). Numerical experiments demonstrate that our method can remove speckle noise efficiently while suppress staircase effects on both synthetic images and real ultrasound images.


Speckle noise Total variation High-order total variation Alternating direction method with multipliers Generalized Kullback-Leibler divergence Ultrasound images 


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The authors would like to thank Meriem Hacini (Laboratoire d’ Automatique et de Robotique, Algeria) for providing the real ultrasound images. This research is supported by 973 Program (2013CB329404), NSFC (61370147, 11401081, 61402082).


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  2. 2.School of Mathematical SciencesUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

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