FDDL-Net: frequency domain decomposition learning for speckle reduction in ultrasound images

Tongda YANG, Weiming WANG, Gary CHENG*, Mingqiang WEI, Haoran XIE, Fu Lee WANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)

Abstract

Image decomposition is a useful operation that benefits a number of low-level vision tasks. However, this conventional wisdom is not well studied in deep learning, and almost no existing deep learning-based methods consider the fact that the extracted feature map from a convolution layer consists of different frequency information. We propose an end-to-end frequency domain decomposition learning network (FDDL-Net) to remove speckle noise from ultrasound images. FDDL-Net leverages frequency domain decomposition at the feature level to learn structure and detail information from ultrasound images via an interactive dual-branch framework. According to the properties of speckle noise, the median filter is utilized in the high-frequency branch of the network to remove the noise effectively. In addition, information from the two branches is exchanged interactively, so that valuable features from different frequencies are fully exploited for speckle reduction. Compared with state-of-the-art methods, FDDL-net demonstrates superior noise reduction and feature preservation (0.89 and 30.92 for SSIM and PSNR metrics respectively), attributing to the dual-branch interaction of the network.
Original languageEnglish
Pages (from-to)42769-42781
Number of pages13
JournalMultimedia Tools and Applications
Volume81
Issue number29
Early online date10 Aug 2022
DOIs
Publication statusPublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Dual-branch interaction
  • FDDL-Net
  • Speckle noise removal
  • Ultrasound images

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