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Remote sensing image denoising based on deformable convolution and attention-guided filtering in progressive framework

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Abstract

Remote sensing image denoising tasks are challenged by complex noise distributions and multiple noise types, including a mixture of additive Gaussian white noise (AWGN) and impulse noise (IN). For better image recovery, complex contextual information needs to be balanced while maintaining spatial details. In this paper, a denoising model based on multilevel progressive image recovery is proposed to address the problem of remote sensing image denoising. In our model, the deformable convolution improves spatial feature sampling to effectively capture image details. Meanwhile, attention-guided filtering is used to pass the output images from the first and second stages to the third stage in order to prevent information loss and optimize the image recovery effect. The experimental results show that under the mixed noise scene of Gaussian and pepper noise, our proposed model shows superior performance relative to several existing methods in terms of both visual effect and objective evaluation indexes. Our model can effectively reduce the influence of image noise and recover more realistic image details.

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Data Availability

No datasets were generated or analysed during the current study.

References

  1. Jiang, J., Zhang, L., Yang, J.: Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans. Image Process. 23(6), 2651–2662 (2014)

    Article  MathSciNet  Google Scholar 

  2. Huang, T., Dong, W., Xie, X., Shi, G., Bai, X.: Mixed noise removal via Laplacian scale mixture modeling and nonlocal low-rank approximation. IEEE Trans. Image Process. 26(7), 3171–3186 (2017)

    Article  MathSciNet  Google Scholar 

  3. Islam, M.T., Rahman, S.M.M., Ahmad, M.O., Swamy, M.N.S.: Mixed Gaussian-impulse noise reduction from images using convolutional neural network. Signal Process. Image Commun. 68, 26–41 (2018)

    Article  Google Scholar 

  4. Thakur, R.K., Maji, S.K.: AGSDNet: attention and gradient-based sar denoising network. IEEE Geosci. Remote Sens. Lett. 19(4506805), 1–5 (2022)

    Google Scholar 

  5. Hussein, A.A., Hussain, S., Reja, A.: Image mixed gaussian and impulse noise elimination based on sparse representation model. Indones. J. Electr. Eng. Comput. Sci. 23(1440), 1440–1450 (2021)

    Google Scholar 

  6. Thakur, R. K., Maji, S. K.: SIFSDNet: Sharp image feature based SAR denoising network. In: IGARSS 2022–2022 IEEE international geoscience and remote sensing symposium, pp. 3428–3431, Kuala Lumpur, Malaysia (2022)

  7. Buades, A., Coll, B., Morel, J.-M.: Nonlocal image and movie denoising. Int. J. Comput. Vis. 76(2), 123–139 (2008)

    Article  Google Scholar 

  8. Liu, L., Chen, L., Chen, C.L.P., Tang, Y.Y., Pun, C.M.: Weighted joint sparse representation for removing mixed noise in image. IEEE Trans. Cybern. 47(3), 600–611 (2017)

    Article  Google Scholar 

  9. Liu, L., Chen, C.L.P., You, X., Tang, Y.Y., Zhang, Y., Li, S.: Mixed noise removal via robust constrained sparse representation. IEEE Trans. Circuits Syst. Video Techn. 28(9), 2177–2189 (2018)

    Article  Google Scholar 

  10. Barbu, T.: nonlinear hyperbolic pde-based filter for mixed poisson-gaussian noise removal from X-ray images. In: 2022 E-health and bioengineering conference (EHB), pp. 1–4, Iasi, Romania (2022)

  11. Zhang, L., Qian, Y., Han, J., Duan, P., Ghamisi, P.: Mixed noise removal for hyperspectral image with l0 l1−2 SSTV regularization. J Sel. Topics Appl. Earth Observ. Remote Sens. 15, 5371–5387 (2022)

    Article  Google Scholar 

  12. Xu, J., Ren, D., Zhang, L., Zhang, D.: Patch group based bayesian learning for blind image denoising. In: computer vision–ACCV 2016 workshops, pp. 79–95. Beijing, China (2016)

  13. Maji, S.K., Mahajan, A.: A Joint denoising technique for mixed gaussian-impulse noise removal in HSI. IEEE Geosci. Remote Sens. Lett. 20(5503605), 1–5 (2023)

    Article  Google Scholar 

  14. Zhuang, L., Ng, M. K.: FastHyMix: fast and parameter-free hyperspectral image mixed noise removal. lEEE Trans. Neural Netw. Learn Syst. 34(8), 4702–4716(2023)

  15. Chen, R., Pu, D., Tong, Y., Wu, M.: Image-denoising algorithm based on improved K-singular value decomposition and atom optimization. CAAI Trans. Intell. Technol. 7(1), 117–127 (2022)

    Article  Google Scholar 

  16. Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: 25th international conference on neural information processing systems, pp. 350–358. NIPS'12, Red Hook, NY, USA (2012)

  17. Burger, H. C., Schuler, C. J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: 2012 IEEE conference on computer vision and pattern recognition (CVPR), pp. 2392–2399. IEEE RI, USA (2012)

  18. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 314–3155 (2017)

    Article  MathSciNet  Google Scholar 

  19. Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: 30th international conference on neural information processing systems, pp. 2802–2810. NIPS, Red Hook, NY, USA (2016)

  20. Lyu, Q., Mao, M., Zhao, P.: DeGAN: Mixed noise removal via generative adversarial networks. Appl. Soft Comput. 95, 106478 (2020)

    Article  Google Scholar 

  21. Quan,Y., Chen, M., Pang, T., Ji, H.: Self2Self With Dropout: learning self-supervised denoising from single image,. In: 2020 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp. 1887–1895. IEEE, Seattle, WA, USA (2020)

  22. Zamir, S. W. et al.: Multi-stage progressive image restoration. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp.14816–14826. IEEE, Nashville, TN, USA (2021)

  23. Jain, V., Seung, S.: Natural image denoising with convolutional networks. NIPS. 21, 769–776 (2008)

    Google Scholar 

  24. Tai,Y., Yang, J., Liu, X.: MemNet: A persistent memory network for image restoration, In: 2017 IEEE international conference on computer vision (ICCV), pp. 4549–4557. IEEE, Venice, Italy (2017)

  25. Zhang, K., Zuo, W., Zhang, L.: FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27, 4608–4622 (2018)

    Article  MathSciNet  Google Scholar 

  26. Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., Liu, H.: Attention-guided CNN for image denoising. Neural Netw. 124, 117–129 (2020)

    Article  Google Scholar 

  27. Zhang, K., Li, Y., Liang,J., Cao, J., Zhang, Y., Tang, H., Timofte, R., Gool, L.: Practical blind denoising via swin-conv-UNet and data synthesis (2022). arXiv Preprint, arXiv:2203.13278

  28. Chen, Y., Wang,Z., Peng,Y., Zhang, Z., Yu,G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp. 7103–7112. IEEE, Salt Lake City, UT, USA (2018)

  29. Cheng, B., Chen, L., Wei,Y., Zhu, Y., Huang, Z., Xiong, J., Huang, T., Hwu, W., Shi, H.: SPGNet: semantic prediction guidance for scene parsing. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp. 5217–5227. IEEE, Seoul, Korea (2019)

  30. Farha, Y. A., Gall, J.: MS-TCN: multi-stage temporal convolutional network for action segmentation. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 3570–3579. IEEE, Long Beach, CA, USA (2019)

  31. Ronneberger, O., Fischer, P., Brox, T.: UNet: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention-MICCAI, pp. 234–241. Springer, Munich, Germany (2015)

  32. Gan, T., Lu, W.: Image denoising using multi-stage sparse representations. In: 2010 IEEE international conference on image processing, pp.1165–1168. IEEE, Hong Kong, China (2010)

  33. Suin, M., Purohit, K., Rajagopalan, A. N. (2020): Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: 2020 IEEE/CVF conference on computer vision and pattern recognition, pp. 3603 3612, IEEE, Seattle, WA, USA

  34. Song, H., Chen, L., Cui, Y., Li, Q., Wang, Q., Fan, J., Yang, J., Zhang, L.: Denoising of MR and CT images using cascaded multi-supervision convolutional neural networks with progressive training. Neurocomputing 469, 354–365 (2022)

    Article  Google Scholar 

  35. Tian, C., Zheng, M., Zuo, W., Zhang, B., Zhang, Y., Zhang, D.: Multi-stage image denoising with the wavelet transform. Pattern Recognit. 134, 109050 (2023)

    Article  Google Scholar 

  36. Bai, Y., Liu, M., Yao, C., Lin, C., Zhao, Y.: MSPNet: Multi-stage progressive network for image denoising. Neurocomputing 517, 71–80 (2023)

    Article  Google Scholar 

  37. Dai, J., et al.: Deformable convolutional networks. In: 2017 IEEE international conference on computer vision (ICCV), pp. 764–773. IEEE, Venice, Italy (2017)

  38. Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable ConvNets V2: more deformable, better results. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 9300–9308. IEEE, Long Beach, CA, USA (2019)

  39. Chang, M., Li, Q., Feng, H., Xu, Z.: Spatial-adaptive network for single image denoising. In: European conference on computer vision (ECCV) ,pp. 171–187. Springer, Glasgow, UK (2020)

  40. Zhou, J., Jampani, V., Pi, Z., Liu, Q., Yang, M. H.: Decoupled dynamic filter networks. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 6643–6652. IEEE, Nashville, TN, USA (2021)

  41. Shen, H., Zhao, Z., Zhang, W.: Adaptive dynamic filtering network for image denoising (2022). arXiv Preprint, arXiv.2211.12051

  42. Abiko, R., Ikehara, M.: Blind denoising of mixed gaussian-impulse noise by single CNN. In: 2019 IEEE international conference on acoustics. Speech and Signal Processing (ICASSP), pp. 1717–1721. IEEE, Brighton, UK (2019)

    Google Scholar 

  43. Islam, M. T., Saha, D., Mahbubur Rahman, S. M., Omair Ahmad, M.,Swamy, M. N. S.: A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images. In: 2018 10th international conference on electrical and computer engineering (ICECE), pp. 97–100. IEEE, Dhaka, Bangladesh (2018)

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No.12171054, the Natural Science Foundation of Jilin Provincial Department of Science and Technology under Grant No.20240101298JC, and in part by the Jilin Provincial Department of Education Science and Technology Project under Grant JJKH20230788KJ.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun, 130022, China

    Hualin Liu, Zhe Li, Shijie Lin & Libo Cheng

  2. Laboratory of Remote Sensing Technology and Big Data Analysis, Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, 528437, China

    Hualin Liu, Zhe Li & Libo Cheng

Authors
  1. Hualin Liu
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  2. Zhe Li
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Contributions

Conceptualization, Z.L. and H.L.; methodology, Z.L. and H.L.; software, S.L. and H.L.; validation, H.L., Z.L. and S.L.; formal analysis, Z.L.; investigation, H.L.; resources, Z.L.; data curation, H.L.; writing—original draft preparation, H.L.; writing—review and editing, Z.L. and H.L.; visualization, H.L.; supervision, Z.L.; project administration, Z.L. and L.C.; funding acquisition, Z.L. and L.C. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zhe Li.

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Liu, H., Li, Z., Lin, S. et al. Remote sensing image denoising based on deformable convolution and attention-guided filtering in progressive framework. SIViP 18, 8195–8205 (2024). https://doi.org/10.1007/s11760-024-03461-1

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