SAR Image Despeckling with Residual-in-Residual Dense Generative Adversarial Network

Yunpeng Bai*, Yayuan Xiao, Xuan Hou, Ying Li, Changjing Shang, Qiang Shen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

1 Citation (SciVal)

Abstract

Deep convolutional neural networks have delivered remarkable aptitude in performing Synthetic Aperture Radar (SAR) image speckle removal tasks. Such approaches are nevertheless constrained in balancing speckle removal and preservation of spatial information, particularly with respect to strong speckle noise. In this paper, a novel residual-in-residual dense generative adversarial network is proposed to effectively suppress SAR image speckle while retaining rich spatial information. A despeckling sub-network composed of residual-in-residual dense blocks with an encoder-decoder structure is devised to learn end-to-end mapping of noisy images onto noise-free images, where the combination of residual-in-residual structure and dense connection significantly enhances the feature representation capability. In addition, a discriminator sub-network with a fully convolutional structure is introduced, and the adversarial learning strategy is adopted to continuously refine the quality of despeckled results. Systematic experimental results on simulated and real SAR images demonstrate that the novel approach offers superior performance in both quantitative and visual evaluation as compared to state-of-the-art methods.

Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOIs
Publication statusPublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 04 Jun 202310 Jun 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period04 Jun 202310 Jun 2023

Keywords

  • dense connection
  • despeckling
  • generative adversarial network
  • residual learning
  • SAR

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