The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
권호 표지
DOI QR코드

DOI QR Code

Non-homogeneous noise removal for side scan sonar images using a structural sparsity based compressive sensing algorithm

구조적 희소성 기반 압축 센싱 알고리즘을 통한 측면주사소나 영상의 비균일 잡음 제거

  • Received : 2017.12.11
  • Accepted : 2018.01.30
  • Published : 2018.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

The quality of side scan sonar images is determined by the frequency of a sonar. A side scan sonar with a low frequency creates low-quality images. One of the factors that lead to low quality is a high-level noise. The noise is occurred by the underwater environment such as equipment noise, signal interference and so on. In addition, in order to compensate for the transmission loss of sonar signals, the received signal is recovered by TVG (Time-Varied Gain), and consequently the side scan sonar images contain non-homogeneous noise which is opposite to optic images whose noise is assumed as homogeneous noise. In this paper, the SSCS (Structural Sparsity based Compressive Sensing) is proposed for removing non-homogeneous noise. The algorithm incorporates both local and non-local models in a structural feature domain so that it guarantees the sparsity and enhances the property of non-local self-similarity. Moreover, the non-local model is corrected in consideration of non-homogeneity of noises. Various experimental results show that the proposed algorithm is superior to existing method.

측면주사소나 영상의 화질은 소나 운용 주파수의 영향을 받는다. 저주파 측면주사소나 장비로 얻는 영상은 저화질 영상이며, 잡음이 화질 저하의 요소 중 하나가 된다. 균일한 잡음을 가정하는 광학 영상과는 달리. 측면주사소나 데이터의 잡음은 해양 환경(장비 소음, 신호 간섭 등)에 의해 발생한다. 또한 소나 신호의 전달 손실을 보상하고자 시간변환이득(Time-Varied Gain, TVG)을 수행하며, 이로 인해 측면주사소나 영상에 비균일 잡음이 생성된다. 본 논문에서는 측면주사소나 영상에 포함된 비균일 잡음을 제거하는 구조적 희소성에 기반한 압축 센싱 알고리즘 (Structural Sparsity based Compressive Sensing, SSCS)을 제안한다. 영상의 구조적 특징 도메인에서 국부적 및 비국부적 모델링을 동시에 구현하여 계수의 희소성을 보장하면서 비국부적 자가 유사성을 강화한다. 그리고 잡음의 비균일성을 고려하여 비국부적 모델링을 보상한다. 다양한 모의 실험을 통해 제안한 알고리즘의 우수성을 입증한다.

Keywords

References

  1. Y. Chen, K. Lee, B. Ku, S. Lee, S. Kim, and H, Ko, "Analyze the sonar image according to the frequency and altitude of side scan sonar," KSNVE, 308 (2017).
  2. M. Moszynski and A. Stepnowski, "Increasing the accuracy of time-varied-gain in digital echosounders," Acta Acustica United with Acustica, 88, 814-817 (2002).
  3. M. Aharon, M. Elad, and A. M. Bruckstein, "The {K-SVD}: An algorithm for designing of overcomplete dictionaries for sparse representations," IEEE Trans. Signal Process., 54, 4311-4322 (2006). https://doi.org/10.1109/TSP.2006.881199
  4. W. Dong, X. Li, L. Zhang, and G. Shi, "Sparsity-based image denoising via dictionary learning and structural clustering," Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 457-464 (2011).
  5. J. Lv and F. Wang, "Image laplace denoising based on sparse representation," International Conference on Computational Intelligence and Communication Networks, 373-377 (2015).
  6. P. Chatterjee and P. Milanfar, "Clustering-based denoising with locally learned dictionaries," Image Processing, IEEE transactions on, 18, 1438-1451 (2009).
  7. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Non-local sparse models for image restoration," in IEEE 12th Int. Conf. on Computer Vision, 2272-2279, (2009).
  8. M. Yang and L. Zhang, "Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary," in IEEE 11th Eur. Conf. on Computer vision, 448-461 (2010).
  9. L. W. Kang, C. Y. Hsu, H. W. Chen, C. S. Lu, C. Y. Lin, and S. C. Pei, "Feature-based sparse representation for image similarity assessment," IEEE Trans. Multimed., 13, 1019-1030, (2011). https://doi.org/10.1109/TMM.2011.2159197
  10. A. Buades, B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," CVPR, 2, 60-65 (2005).
  11. A. Buades, B. Coll, and J. M. Morel, "Neighborhood filters and pde's," CMLA Technical Report 2005-04, (2005).
  12. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, "Image denoising using scale mixtures of Gaussians in thewavelet domain," IEEE Transactions on Image Processing, 12, 1338-1351, (2003). https://doi.org/10.1109/TIP.2003.818640
  13. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-d transform-domain collaborative filtering," IEEE Trans. on Image Processing, 16, 2080-2095 (2007). https://doi.org/10.1109/TIP.2007.901238
  14. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, "An efficient k-means clustering algorithm: analysis and implementation," IEEE Trans. Pattern Anal. Mach. Intell., 24, 881-892 (2002). https://doi.org/10.1109/TPAMI.2002.1017616
  15. H. Abdi and L. J. Williams, "Principal component analysis," Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459 (2010). https://doi.org/10.1002/wics.101
  16. T. Boas, A. Dutta, X. Li, K. Mercier, and E. Niederman, "Shrinkage function and Its applications in matrix approximations," Electronic J. Linear Algebra, 32, 163-171 (2017). https://doi.org/10.13001/1081-3810.3218
  17. O. Tuzel, F. Porikli, and P. Meer, "Pedestrian detection via classification on Riemannian manifolds," IEEE Trans. Pattern Anal. Mach. Intell., 30, 1713-1727 (2008). https://doi.org/10.1109/TPAMI.2008.75
  18. G. Zhao and T. Ahonen, "Rotation invariant image and video description with local binary pattern features," IEEE Tip., 21, 1-13 (2010).
  19. L. Cayton, "Algorithms for manifold learning," Univ. of California at San Diego Tech. Rep., 44(CS2008-0923), 973-980 (2005).