Abstract
During the process of acquisition and transmission, images are often likely to be corrupted by mixed Gaussian-impulse noise. Among various image denoising methods, most traditional methods can only deal with a single type of noise due to the difficulty of modeling the distribution of the mixed noise. In this paper, we propose a novel mixed Gaussian-impulse noise removal method based on adaptive integration skip compensation Network (Ai-Sc-Net). More concretely, a couple of skip compensation networks (Sc-Net) Sc-Net-AWGN and Sc-Net-IN are trained on Gaussian and Impulse noise datasets separately to deal with the corresponding single type noise. Further, an adaptive integration network (Ai-Net) is used to integrate the two outputs of Sc-Net-AWGN and Sc-Net-IN. The Ai-Sc-Net is then be constructed based on Sc-Net and Ai-Net, which can handle mixed noise. Experimental results in synthetic noise images have shown great improvements over several state-of-the-art mixed noise removal methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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 (2016)
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)
Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620 (2013). A Publication of the IEEE Signal Processing Society
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC2012) Results (2012). http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
Fan, H., Chen, Y., Guo, Y., Zhang, H., Kuang, G.: Hyperspectral image restoration using low-rank tensor recovery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(10), 4589–4604 (2017)
Garnett, R., Huegerich, T., Chui, C., He, W.: A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14(11), 1747–1754 (2005)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
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)
Jain, V., Seung, H.S.: Natural image denoising with convolutional networks. In: International Conference on Neural Information Processing Systems, pp. 769–776 (2008)
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)
Li, G., Huang, X., Li, S.G.: Adaptive bregmanized total variation model for mixed noise removal. AEU - Int. J. Electron. Commun. 80, 29–35 (2017)
Liu, L., Chen, L., Chen, C.L., Tang, Y.Y., Pun, C.M.: Weighted joint sparse representation for removing mixed noise in image. IEEE Trans. Cybern. 47(3), 600–611 (2017)
Mao, X.J., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections (2016)
Paszke, A., et al.: Automatic differentiation in pytorch. In: NIPS-W (2017)
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_28
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images ICCV. In: Proceedings of Sixth International Conference on ICCV, p. 839 (1998)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(12), 3371–3408 (2010)
Xiao, Y., Zeng, T., Yu, J., Ng, M.K.: Restoration of images corrupted by mixed gaussian-impulse noise via minimization. Pattern Recognit. 44(8), 1708–1720 (2011)
Yan, M.: Restoration of images corrupted by impulse noise and mixed gaussian impulse noise using blind inpainting. SIAM J. Imaging Sci. 6(3), 1227–1245 (2013)
Yu, A.C., Peng, Q.: Robust recognition of checkerboard pattern for camera calibration. Opt. Eng. 45(9), 1173–1183 (2006)
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). A Publication of the IEEE Signal Processing Society
Acknowledgment
This work was supported in part by the Project of National Engineering Laboratory for Video Technology - Shenzhen Division, in part by Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality under Grant ZDSYS 201703031405467, and in part by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing) under Grant 1230233753.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, K., Zhang, Y., Li, T.H., Huang, K., Li, G. (2018). Adaptive Integration Skip Compensation Neural Networks for Removing Mixed Noise in Image. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-00776-8_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00775-1
Online ISBN: 978-3-030-00776-8
eBook Packages: Computer ScienceComputer Science (R0)