Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1125–1132 | Cite as

Ultrasound spatiotemporal despeckling via Kronecker wavelet-Fisz thresholding

  • Younes FaroujEmail author
  • Laurent Navarro
  • Jean-Marc Freyermuth
  • Marianne Clausel
  • Philippe Delachartre
Original Paper


We propose a novel framework for despeckling ultrasound image sequences while respecting the structural details. More precisely, we use thresholding in an adapted wavelet domain that jointly takes into account for the non-Gaussian statistics of the noise and the differences in spatial and temporal regularities. The spatiotemporal wavelet is obtained via the Kronecker product of two sparsifying wavelet bases acting, respectively, on the spatial and temporal domains. Besides enabling a structured sparse representation of the time–space plan, it also makes it possible to perform a variance stabilization routine on the spatial domain through a Fisz transformation. The proposed method enjoys adaptability, easy tuning and theoretical guaranties. We propose the corresponding algorithm together with results that demonstrate the benefits of the proposed spatiotemporal approach over the successive spatial treatment. Finally, we describe a data-driven extension of the proposed method that is based on temporal pre-filtering.


Dynamic ultrasound imaging Despeckling Fisz transformation Variance stabilization Structured sparsity 



This work was supported by “Région Rhône-Alpes” under the ARC 6. L. Navarro’s research was supported by the (ANR) under reference ANR-15-CE19-0002 (LBSMI). P. Delachartre was within the framework of the Labex CELYA (ANR-10-LABX-0060) and Labex PRIMES (ANR-11-LABX-0063) of the Université de Lyon.

Supplementary material

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Medical Image Processing Lab, Institute of BioengineeringEPFLLausanneSwitzerland
  2. 2.CNRS UMR 5307; LGFÉcole Nationale Supérieure des MinesSaint-ÉtienneFrance
  3. 3.Institute of StatisticsUniversité de NeuchâtelNeuchâtelSwitzerland
  4. 4.Laboratoire Jean Kuntzmann, CNRS UMR 5224Université de GrenobleGrenobleFrance
  5. 5.CREATIS; CNRS UMR5220; Inserm U1044; INSA-LyonUniversité de LyonLyonFrance

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