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 language | English |
|---|---|
| Article number | 8653394 |
| Pages (from-to) | 1215-1219 |
| Number of pages | 5 |
| Journal | IEEE Geoscience and Remote Sensing Letters |
| Volume | 16 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2019 Aug 1 |
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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
Cite this
Nonhomogeneous Noise Removal from Side-Scan Sonar Images Using Structural Sparsity. / Jin, Youngsaeng; Ku, Bonhwa; Ahn, Jaekyun; Kim, Seongil; Ko, Hanseok.
In: IEEE Geoscience and Remote Sensing Letters, Vol. 16, No. 8, 8653394, 01.08.2019, p. 1215-1219.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Nonhomogeneous Noise Removal from Side-Scan Sonar Images Using Structural Sparsity
AU - Jin, Youngsaeng
AU - Ku, Bonhwa
AU - Ahn, Jaekyun
AU - Kim, Seongil
AU - Ko, Hanseok
PY - 2019/8/1
Y1 - 2019/8/1
N2 - 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.
AB - 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.
KW - Compressive sensing (CS)
KW - image denoising
KW - nonhomogeneous noise
KW - side-scan sonar (SSS)
KW - structural sparsity
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UR - http://www.scopus.com/inward/citedby.url?scp=85069452854&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2019.2895843
DO - 10.1109/LGRS.2019.2895843
M3 - Article
AN - SCOPUS:85069452854
VL - 16
SP - 1215
EP - 1219
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
IS - 8
M1 - 8653394
ER -