Ultrasound spatiotemporal despeckling via Kronecker wavelet-Fisz thresholding
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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.
KeywordsDynamic 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.
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