Nonhomogeneous Noise Removal from Side-Scan Sonar Images Using Structural Sparsity

Youngsaeng Jin, Bonhwa Ku, Jaekyun Ahn, Seongil Kim, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The image quality of side-scan sonar (SSS) is determined by its operating frequency. SSS operating at a low frequency produces low-quality images due to high levels of noise. This noise is randomly generated from a number of different sources, including equipment noise and underwater environmental interference. In addition, to compensate for transmission loss in a received signal, the signal is amplified by time-varied gain correction, and consequently, SSS images contain nonhomogeneous noise, unlike natural images whose noise is assumed to be homogeneous. In this letter, a structural sparsity-based image denoising algorithm is proposed to remove nonhomogeneous noise from SSS images. The algorithm incorporates both local and nonlocal models in the structural features domain in order to guarantee sparsity and enhance nonlocal self-similarity. Using structural features also preserves fine-scale structures, leading to denoised images with natural seabed textures. The patch weights in the nonlocal model are corrected in consideration of the nonhomogeneity of the noise. Experimental results show that the proposed algorithm is qualitatively and quantitatively comparable to conventional algorithms.

Original languageEnglish
Article number8653394
Pages (from-to)1215-1219
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume16
Issue number8
DOIs
Publication statusPublished - 2019 Aug 1

Keywords

  • Compressive sensing (CS)
  • image denoising
  • nonhomogeneous noise
  • side-scan sonar (SSS)
  • structural sparsity

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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