Abstract
Aiming at limiting drawbacks of denoising algorithms that can only remove one or two specific types of noise (and which are inefficient for other types), we propose a combined neural-network model for mixed-noise removal in images. Nine convolutional layers are adapted, and noisy images are trained through feature extraction, shrinking, nonlinear mapping, expanding, and reconstruction. Experimental results show that the algorithm achieves better denoising results and is more suitable than other algorithms for dealing with different types of mixed noise in images. Subjective visual effects and an objective evaluation demonstrate the achieved improvements.
This work is supported by the National Natural Science Foundation of China (61540059, 41671441, 91120002); Plan Project of Guangdong Provincial Science and technology(2015B010131007); Hubei Provincial Department of Education Guiding Project (B2016187).
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References
Jiang, J., Zhang, L., Yang, J.: Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans. Image Process. 23, 2651–2662 (2014)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing over complete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Dabov, K., Foi, A., Katkovnik, V., et al.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Knaus, C., Zwicker, M.: Dual-domain image denoising. In Proceedings of IEEE International Conference Image Processing, pp. 440–444 (2013)
Xiao, J., Li, W., Jiang, H., Peng, H., Zhu, S.: Three dimensional block-matching video denoising algorithm based on dual-domain filtering. J. Commun. 9, 91–97 (2015)
Hwang, H., Haddad, R.A.: Adaptive median filters: new algorithms and results. IEEE Trans. Image Process. 4(4), 499–502 (1995)
Ko, S.J., Lee, Y.H.: Center weighted median filters and their applications to image enhancement. IEEE Trans. Circ. Syst. 38(9), 984–993 (1991)
Chen, T., Wu, H.R.: Space variant median filters for the restoration of impulse noise corrupt-ed images. IEEE Trans. Circ. Syst. II: Analog Digital Signal Process. 48(8), 784–789 (2001)
Cai, J.F., Chan, R.H., Nikolova, M.: Fast two-phase image deblurring under impulse noise. J. Math. Imaging Vis. 36(1), 46–53 (2010)
Mitra, K., Veeraraghavan, A., Chellappa, R.: Robust RVM regression using sparse outlier model. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1887–1894. IEEE (2010)
Liu, J., Tai, X.C., Huang, H., et al.: A weighted dictionary learning model for denoising images corrupted by mixed noise. IEEE Trans. Image Process. 22(3), 1108–1120 (2013)
Aggarwal, H.K., Majumdar, A.: Hyperspectral image denoising using spatio-spectral total variation. IEEE Geosci. Remote Sens. Lett. 13(3), 442–446 (2016)
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 (2012)
Xiao, J., Liu, T., Zhang, Y., et al.: Multi-focus image fusion based on depth extraction with inhomogeneous diffusion equation. Signal Process. 125(C), 171–186 (2016)
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25
Dong, C., Chen, C.L., He, K., et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Acknowledgement
I would like to extend my heartfelt thanks to a host of people without whose assistance the accomplishment of this paper would have been impossible. They are Bijun Li, Jian Zhou and my supervisor Huyin Zhang. I am also grateful to Reinhard Klette (Auckland), whose valuable instruction has benefited me a great deal. Authors thank Reinhard Klette (Auckland University of Technology, New Zealand) for comments on the paper.
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Ding, L., Zhang, H., Li, B., Zhou, J., Gu, W. (2018). Mixed-Noise Removal in Images Based on a Convolutional Neural Network. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_35
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DOI: https://doi.org/10.1007/978-3-319-92753-4_35
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