All Issue

2020 Vol.39, Issue 4 Preview Page

Research Article

Denoising ISTA-Net: learning based compressive sensing with reinforced non-linearity for side scan sonar image denoising

Denoising ISTA-Net: 측면주사 소나 영상 잡음제거를 위한 강화된 비선형성 학습 기반 압축 센싱

Bokyeung Lee1
,
Bonwha Ku1
,
Wan-Jin Kim2
,
Seongil Kim2
,
Hanseok Ko1∗
이 보경1
,
구 본화1
,
김 완진2
,
김 성일2
,
고 한석1∗
1고려대학교
2국방과학연구소
∗Corresponding Author
31 July 2020. pp. 246-254
PDF XML Citation
Abstract

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

Keywords
Side scan sonar
Image denoising
Compressive sensing
Learning based compressive sensing

본 논문에서는 학습 기반 압축 센싱 기법을 이용한 측면주사 소나 영상의 비균일 잡음 제거 알고리즘을 제안한다. 제안하는 기법은 Iterative Shrinkage and Thresholding Algorithm(ISTA) 알고리즘을 기반으로 하고 있으며 성능 향상을 위해 학습네트워크의 비선형성을 강화시키는 전략을 선택하였다. 제안된 구조는 입력 신호를 비선형 변환과 초기화 하는 부분, Sparse 공간으로 변환 및 역변환하는 ISTA block, 특징 공간에서 픽셀 공간으로 변환하는 부분으로 구성된다. 제안된 기법은 다양한 모의 실험을 통해 잡음 제거 성능 및 메모리 효율성 측면에서 우수함이 입증되었다.

키워드
측면주사 소나
영상 잡음 제거
압축 센싱
학습기반 압축 센싱
References
1
Y. Chen, K. Lee, B. Ku, S. Kim, and H. Ko, "Analyze the sonar image according to the frequency and altitude of side scan sonar," Proc. Korean Soc. Noise Vib. Eng. 2017, 308 (2017).
2
M. Aharon and M. Elad, "Image denoising via sparse and redundant representations over learned dictinaries," Proc. IEEE Trans. on Image Process. 15, 3736-3745 (2006).
10.1109/TIP.2006.88196917153947
3
W. Dong, X. Li, L. Zhang, and G. Shi, "Sparsity-based image denoising via dictionary learning and structural clustersing," Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 457-464 (2011).
10.1109/CVPR.2011.5995478
4
J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," Proc. IEEE Trans. on information theory, 4655-4666 (2007).
10.1109/TIT.2007.909108
5
K. Gregor and Y. LeCun, "Learning fast approximations of sparse coding," Proc. 27th International Conf. on Machine Learning, 399-406 (2010).
6
J. Zhang and B. Ghanem, "ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing," Proc. IEEE Cof. computer vision and pattern recognition, 1828-1837 (2018).
10.1109/CVPR.2018.00196
7
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proc. IEEE Cof. computer vision and pattern recognition, 770-778 (2016).
10.1109/CVPR.2016.9026180094
8
O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," Proc. IEEE Cof. Medical image computing and computer-assisted intervention, 234-241 (2015).
10.1007/978-3-319-24574-4_28
9
C. Szegedy, S. Ioffe, V. Vangoucke, and A. Alemi, "Inception-v4, inception-resnet and the impact of residual connections on learning," Proc. Cof. 34th AAAI on Artificial Intelligence, 4278-4284 (2017).
10
P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, "Extracting and composing robust features with denoising autoencoders," Proc. the 25th international Conf. on Machine learning, 1096-1103 (2008).
10.1145/1390156.1390294
11
D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint, 1412.6980 (2014).
12
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-D transform domain collavorative filtering," Proc. IEEE Trans. on Image Processing, 16, 2080-2095 (2007).
10.1109/TIP.2007.90123817688213
13
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, "Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising," Proc. IEEE Trans. on Image Processing, 26, 3142-3155 (2017).
10.1109/TIP.2017.266220628166495
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 : 39
  • No :4
  • Pages :246-254
  • Received Date : 2020-04-09
  • Revised Date : 2020-05-22
  • Accepted Date : 2020-05-26
  • DOI :https://doi.org/10.7776/ASK.2020.39.4.246
Journal Informaiton The Journal of the Acoustical Society of Korea The Journal of the Acoustical Society of Korea
E-Book
  • scopus_JASK
  • ESCI_JASK
  • Google scholar_JASK
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • ccl
Journal Informaiton Journal Informaiton - close