Opto-Electronic Engineering, Volume. 50, Issue 6, 230017(2023)
Sonar image denoising method based on residual and attention network
[1] [1] Zeng T, Ren L L, Wang Y J, et al. Feature-based underwater three-dimensional sonar target detection and tracking algorithm[J]. Acta Armam. https://doi.org/10.12382/bgxb.2022.0017.
[6] [6] Wu D, Du X, Wang K Y. An effective approach for underwater sonar image denoising based on sparse representation[C]//Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing. Chongqing: IEEE, 2018: 389–393. https://doi.org/10.1109/ICIVC.2018.8492877.
[7] [7] Jiang S Y, Xing C X, Wan Z L, et al. Research on multiplicative speckle noise denoising method of side-scan sonar image based on analysis sparse decomposition[C]//Proceedings of 2021 OES China Ocean Acoustics. Harbin: IEEE, 2021: 1016–1020. https://doi.org/10.1109/COA50123.2021.9519941.
[8] [8] Chen M, Li L, Li Z J, et al. Research on sonar image denoising method based on fixed water area noise model[C]//Proceedings of 2021 IEEE International Conference on Mechatronics and Automation. Takamatsu: IEEE, 2021: 231–235. https://doi.org/10.1109/ICMA52036.2021.9512575.
[9] [9] Stolojescu-Crisan C, Isar A. A new automatic conditioning algorithm for SONAR images[C]//Proceedings of 2021 International Symposium on Signals, Circuits and Systems. Iasi: IEEE, 2021: 1–4. https://doi.org/10.1109/ISSCS52333.2021.9497424.
[15] [15] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770–778. https://doi.org/10.1109/CVPR.2016.90.
[17] [17] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2261–2269. https://doi.org/10.1109/CVPR.2017.243.
[18] [18] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 105–114. https://doi.org/10.1109/CVPR.2017.19.
[20] [20] Cheng S, Wang Y Z, Huang H B, et al. NBNet: noise basis learning for image denoising with subspace projection[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 4894–4904. https://doi.org/10.1109/CVPR46437.2021.00486.
[21] [21] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015. https://doi.org/10.1007/978-3-319-24574-4_28.
[23] [23] Wang Q L, Wu B G, Zhu P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155.
[24] [24] Li X, Wang W H, Hu X L, et al. Selective kernelnetworks[C]//Proceedings of 2019 IEEE/CVF Conference onComputer Vision and Pattern Recognition. Long Beach: IEEE,2019: 510–519. https://doi.org/10.1109/CVPR.2019.00060.
[27] [27] Guo S, Yan Z F, Zhang K, et al. Toward convolutional blind denoising of real photographs[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 1712–1722. https://doi.org/10.1109/CVPR.2019.00181.
[34] [34] Giacomo G, Machado M, Drews P, et al. Sonar-to-satellite translation using deep learning[C]//Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications. Orlando: IEEE, 2018: 454–459. https://doi.org/10.1109/ICMLA.2018.00074.
[37] [37] Ren C, He X H, Wang C C, et al. Adaptive consistency prior based deep network for image denoising[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 8592–8602. https://doi.org/10.1109/CVPR46437.2021.00849.
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Dongdong Zhao, Yifei Ye, Peng Chen, Ronghua Liang, Tiancheng Cai, Xinxin Guo. Sonar image denoising method based on residual and attention network[J]. Opto-Electronic Engineering, 2023, 50(6): 230017
Category: Article
Received: Jan. 20, 2023
Accepted: Apr. 11, 2023
Published Online: Aug. 9, 2023
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