Abstract
In order to achieve effective speckle suppression, we propose a multi-level residual attention network by combining with multi-level block and residual channel attention network, which is suitable for speckle suppression. Firstly, the network model performs a simple shallow feature extraction for the input noise image through two convolution layers. Then, the residual attention network is used to extract the deep features. Finally, a convolution layer and residual learning are used to generate the final denoised image. Experimental results show that the proposed method can effectively suppress the noise and preserve the edge details of the image.
The first author is a student.
This research was funded by National Natural Science Foundation of China under grant 62172139, the Post-graduate’s Innovation Fund Project of Heibei University under grant HBU2021ss002, Natural Science Foundation of Hebei Province under grant F2018210148, F2019201151 and F2020201025, Science Research Project of Hebei Province under grant BJ2020030.
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References
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., Papathanassiou, K.P.: A tutorial on synthetic aperture radar. IEEE Geosci. Remote Sens. Mag. 1(1), 6–43 (2013)
Frost, V.S., Stiles, J.A., Shanmugan, K.S., Holtzman, J.C.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982)
Lee, J.S., Jurkevich, L., Dewaele, P., Wambacq, P., Oosterlinck, A.: Speckle filtering of synthetic aperture radar images : a review. Remote Sens. Rev. 8(4), 313–340 (1994)
Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60–65 (2005)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. In: Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, vol. 6064, p. 606414 (2006)
Parrilli, S., Poderico, M., Angelino, C.V., Verdoliva, L.: A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50(2), 606–616 (2012)
Liu, S.Q., Hu, S.H., Xiao, Y., An, Y.L.: Bayesian Shearlet shrinkage for SAR image de-noising via sparse representation. Multidimension. Syst. Signal Process. 25(4), 683–701 (2013). https://doi.org/10.1007/s11045-013-0225-8
Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: FFA-Net: feature fusion attention network for single image dehazing. Proceedings of the AAAI Conference on Artificial Intelligence 34, 11908–11915 (2020)
Chierchia, G., Cozzolino, D., Poggi, G., Verdoliva, L.: SAR image despeckling through convolutional neural networks. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5438–5441 (2017)
Wang, P., Zhang, H., Patel, V.M.: Generative adversarial network-based restoration of speckled SAR images. In: 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 1–5 (2017)
Wang, P., Zhang, H., Patel, V.M.: SAR image despeckling using a convolutional neural network. IEEE Signal Process. Lett. 24(12), 1763–1767 (2017)
Zhang, Q., Yuan, Q., Li, J., Yang, Z., Ma, X.: Learning a dilated residual network for SAR image despeckling. Remote Sens. 10(2), 196 (2018)
Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 270–279 (2010)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Lee, J.-S.: Speckle analysis and smoothing of synthetic aperture radar images. Comput. Graph. Image Process. 17(1), 24–32 (1981)
Ma, X., Shen, H., Zhao, X., Zhang, L.: SAR image despeckling by the use of variational methods with adaptive nonlocal functionals. IEEE Trans. Geosci. Remote Sens. 54(6), 3421–3435 (2016)
Gomez, L., Ospina, R., Frery, A.C.: Unassisted quantitative evaluation of despeckling filters. Remote Sens. 9(4), 389 (2017)
Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)
Liu, S., et al.: SAR speckle removal using hybrid frequency modulations. IEEE Trans. Geosci. Remote Sens. 59, 3956–3966 (2021)
Liu, S., et al.: Convolutional neural network and guided filtering for SAR image denoising. Remote Sens. 11(6), 702 (2019)
Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2808–2817 (2017)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: ICLR 2015 : International Conference on Learning Representations 2015 (2015)
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Lei, Y., Liu, S., Zhang, L., Zhao, L., Zhao, J. (2021). Multi-level Residual Attention Network for Speckle Suppression. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_24
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