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Sonar image denoising based on clustering and Bayesian sparse coding.

Chuanxi Xing1,2, Debiao Bao1,2, Tinglong Huang1,2

  • 1School of Electrical and Information Technology, Yunnan Minzu University, Kunming, China.

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This study introduces a new denoising algorithm for side-scan sonar images (SSI) to improve clarity. The method effectively suppresses mixed noise while preserving crucial image details for better analysis.

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Area of Science:

  • Marine technology
  • Image processing
  • Signal processing

Background:

  • Side-scan sonar images (SSI) suffer from multiplicative speckle and additive noise, degrading quality and hindering interpretation.
  • Effective denoising is crucial for accurate target recognition and scene analysis in sonar imagery.

Purpose of the Study:

  • To develop an advanced denoising algorithm for SSI that addresses mixed noise.
  • To enhance the preservation of structural details and target features in denoised images.

Main Methods:

  • Integration of non-local similar block clustering with Bayesian sparse coding.
  • Utilizing cross-scale structural features and noise statistics with an Equivalent Number of Looks (ENL) metric and improved K-means for patch classification.
  • Employing a joint dictionary training strategy and Bayesian Orthogonal Matching Pursuit (BOMP) for sparse representation.

Main Results:

  • The proposed algorithm effectively suppresses mixed noise (speckle and additive) in SSI.
  • Demonstrated superior performance over classical methods in objective metrics (PSNR, SSIM) and visual quality.
  • Significantly improved preservation of target edges and textures, even under severe noise conditions.

Conclusions:

  • The proposed denoising algorithm offers a robust solution for enhancing SSI quality.
  • It provides a valuable tool for improving target recognition and scene interpretation in marine acoustics.
  • The method's ability to preserve structural details under noise is a key advantage.