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2019 | OriginalPaper | Buchkapitel

IBDNet: Lightweight Network for On-orbit Image Blind Denoising

verfasst von : Ling Li, Junxing Hu, Yijun Lin, Fengge Wu, Junsuo Zhao

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

Verlag: Springer International Publishing

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Abstract

To reduce the data transmission pressure from the satellite to the ground, it is meaningful to process the image directly on the satellite. As the cornerstone of image processing, image denoising exceedingly improves the image quality to contribute to subsequent works. For on-orbit image denoising, we propose an end-to-end trainable image blind denoising network, namely IBDNet. Unlike existing image denoising methods, which either have a large number of parameters or are unable to perform image blind denoising, the proposed network is lightweight due to the residual bottleneck blocks as the main structure. Although our network does not use clean images for training, the experimental results on the public datasets indicate that the blindly denoised image quality of our method can be roughly the same as that of the state-of-the-art denoisers. Furthermore, we deploy the model (513 KB only) on the same equipment as the one on a satellite, which verifies the feasibility of running on the satellite.

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Vorheriges Kapitel Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation

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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65. IEEE (2005)

Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007). https://doi.org/10.1109/TIP.2007.901238MathSciNetCrossRef

Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006). https://doi.org/10.1109/TIP.2006.881969MathSciNetCrossRef

Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392–2399. IEEE (2012)

Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2774–2781 (2014). https://doi.org/10.1109/CVPR.2014.349

Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256–1272 (2017)CrossRef

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), 3142–3155 (2017). https://doi.org/10.1109/TIP.2017.2662206MathSciNetCrossRefMATH

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). https://doi.org/10.1109/TIP.2018.2839891MathSciNetCrossRef

10.

Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, pp. 2802–2810 (2016)

11.

Chen, J., Chen, J., Chao, H., Yang, M.: Image blind denoising with generative adversarial network based noise modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3155–3164 (2018). https://doi.org/10.1109/CVPR.2018.00333

12.

Lehtinen, J., et al.: Noise2noise: learning image restoration without clean data. arXiv preprint arXiv:1803.04189 (2018)

13.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

14.

Ling, L., Junxing, H., Fengge, W., Junsuo, Z.: A research and strategy of remote sensing image denoising algorithms. arXiv preprint arXiv:1905.10236 (2019)

15.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

16.

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791CrossRef

17.

Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)

18.

Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

19.

Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: International Conference on Machine Learning, vol. 30, no. 1, p. 3 (2013)

20.

Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637–1645 (2016). https://doi.org/10.1109/CVPR.2016.181

21.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

22.

Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P.: Can semantic labeling methods generalize to any city? The Inria aerial image labeling benchmark. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 3226–3229. IEEE (2017)

23.

Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126–135 (2017). https://doi.org/10.1109/CVPRW.2017.150

24.

Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: IEEE International Conference on Computer Vision (2001)

Titel: IBDNet: Lightweight Network for On-orbit Image Blind Denoising
verfasst von: Ling Li
Junxing Hu
Yijun Lin
Fengge Wu
Junsuo Zhao
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing
Print ISBN: 978-3-030-30507-9

Electronic ISBN: 978-3-030-30508-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-30508-6_4

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