Side scan sonar image super-resolution using an improved initialization structure

Junyeop Lee; Bon-hwa Ku; Wan-Jin Kim; Hanseok Ko

doi:10.7776/ASK.2021.40.2.121

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2021 Vol.40, Issue 2 Preview Page Next Page

Research Article

Side scan sonar image super-resolution using an improved initialization structure 향상된 초기화 구조를 이용한 측면주사소나 영상 초해상도 영상복원

31 March 2021. pp. 121-129

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Abstract

This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

Keywords

Compressive sensing

Deep learning

Underwater sonar image

Super resolution

본 논문에서는 학습 기반 압축 센싱을 이용하여 측면 주사 소나 영상의 해상도를 향상하는 초해상도 기법을 다룬다. 딥러닝과 압축 센싱이 접목된 학습 기반 압축 센싱은 구조적인 측면에서 피드-포워드(feed forward) 네트워크 형태이며 학습을 통하여 파라미터들을 자동으로 설정하게 된다. 본 논문에서는 초해상도 과정에서 필요한 추가 정보들을 다양한 초기화 방법을 통해 효과적으로 추출할 수 있는 방법을 제안한다. 다양한 모의 실험에서 제안하는 방법은 기존 방식보다 Peak Signal-to-Noise Ratio(PSNR) 및 Structure Similarity Index Measure(SSIM) 지표상 향상된 성능 결과를 나타내었다.

키워드

압축 센싱

딥러닝

수중 소나 영상

초 해상도 복원

References

J. Lee, J. Park, K. Lee, J. Min, G. Kim, B. Lee, B. Ku, D. K. Han, and H. Ko, "FBRNN: Feedback recurrent neural network for extreme image super-resolution," Proc. of the IEEE/CVF conf. on CVPR. 1-8 (2020). 10.1109/CVPRW50498.2020.00252

L. Y. Ma, X. L. He, and Z. S. Huang, "Side scan sonar image super resolution based on sparse representation," Applied Mechanics and Materials, 48, 1049-1052 (2011). 10.4028/www.scientific.net/AMM.48-49.1049

J. Park, B. Ku, Y. Jin, and H. Ko, "Side scan sonar image super resolution via region-selective sparse coding," IEICE Trans. on Information and Systems, 102, 210-213 (2019). 10.1587/transinf.2018EDL8170

B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, "Enhanced deep residual networks for single image super-resolution," Proc. of the IEEE conf. on CVPR. 136-144 (2017). 10.1109/CVPRW.2017.151

J. Zhang and B. Ghanem, "ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing," Proc. of the IEEE conf. on CVPR. 1828-1837 (2018). 10.1109/CVPR.2018.00196

J. Hu, L. Shen, S. Albanie, and G. Sun, "Squeeze-and- excitation networks," Proc. of the IEEE conf. on CVPR. 7132-7141 (2018).

Z. WANG, J. Chen, and S. C. H. Hoi, "Deep learning for image super-resolution: A survey," IEEE Trans. on Pattern Analysis and Machine Intelligence, 1-1 (2020). 10.1109/TPAMI.2020.298216632217470

H. Chen, X. He, L. Qing, and Q. Then, "Single image super-resolution via adaptive transform-based nonlocal self-similarity modeling and learning-based gradient regularization," IEEE Trans. on Multimedia 19, 1702- 1717 (2017). 10.1109/TMM.2017.2688920

W. Dong, L. Zhang, G. Shi, and X. Wu, "Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization," IEEE TIP. 20, 1838-1857 (2011). 10.1109/TIP.2011.210830621278019

Z. WANG and A. C. Bovik, Modern Image Quality Assessment (Morgan & Claypool, San Rafael, 2006), pp. 1-156. 10.2200/S00010ED1V01Y200508IVM003

Information

Publisher :The Acoustical Society of Korea
Publisher(Ko) :한국음향학회
Journal Title :The Journal of the Acoustical Society of Korea
Journal Title(Ko) :한국음향학회지
Volume : 40
No :2
Pages :121-129
Received Date : 2021-01-18
Revised Date : 2021-02-22
Accepted Date : 2021-03-04
DOI :https://doi.org/10.7776/ASK.2021.40.2.121

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

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