Image Denoising Using a Deep Encoder-Decoder Network with Skip Connections

  • Raphaël Couturier
  • Gilles Perrot
  • Michel SalomonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


In many areas images can be corrupted by various types of noise and therefore image denoising is a prerequisite. For example, medical images like the 4D-CT or ultrasound ones, are prone to noise and artifacts that can affect diagnostic confidence. Remote sensing is another field for which image preprocessing is mandatory to improve the quality of source images. Synthetic Aperture Radar (SAR) images are typically corrupted by multiplicative speckle noise. In this paper, a deep neural network able to deal with both additive white Gaussian and multiplicative speckle noises is developed, showing also some blind denoising capacity. The experiments on noisy images show that the proposal, which consists in a encoder-decoder, is efficient and competitive in comparison with state-of-the-art methods.


Image denoising Additive and multiplicative noises Deep learning Encoder-decoder 



This work has been supported by the EIPHI Graduate School (contract “ANR-17-EURE-0002”).


  1. 1.
    Bas, P., Filler, T., Pevný, T.: “Break Our Steganographic System”: the ins and outs of organizing BOSS. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011). Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)MathSciNetCrossRefGoogle Scholar
  5. 5.
    François, D., Wertz, V., Verleysen, M., et al.: Non-euclidean metrics for similarity search in noisy datasets. In: 13th European Symposium on Artificial Neural Networks (ESANN), pp. 339–344 (2005)Google Scholar
  6. 6.
    Gu, F., Zhang, H., Wang, C., Zhang, B.: Residual encoder-decoder network introduced for multisource SAR image despeckling. In: 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), pp. 1–5. IEEE (2017)Google Scholar
  7. 7.
    Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2862–2869 (2014)Google Scholar
  8. 8.
    Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976. IEEE (2017)Google Scholar
  9. 9.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations (ICLR) (2015)Google Scholar
  10. 10.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. IEEE (2015)Google Scholar
  11. 11.
    Ma, K., Duanmu, Z., Wu, Q., Wang, Z., Yong, H., Li, H., Zhang, L.: Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans. Image Process. 26(2), 1004–1016 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Mao, X., Shen, C., Yang, Y.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 2802–2810. Curran Associates, Inc. (2016)Google Scholar
  13. 13.
    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: 8th IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 416–423. IEEE (2001)Google Scholar
  14. 14.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International Conference for Learning Representations (ICLR) (2016)Google Scholar
  15. 15.
    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). Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: 37th Asilomar Conference on Signals, Systems Computers, vol. 2, pp. 1398–1402. IEEE (2003)Google Scholar
  18. 18.
    Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25 (NIPS 2012), pp. 341–349. Curran Associates, Inc. (2012)Google Scholar
  19. 19.
    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)MathSciNetCrossRefGoogle Scholar
  20. 20.
    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)MathSciNetCrossRefGoogle Scholar
  21. 21.
    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. IEEE (2017)Google Scholar
  22. 22.
    Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Image Process. 3(1), 47–57 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Raphaël Couturier
    • 1
  • Gilles Perrot
    • 1
  • Michel Salomon
    • 1
    Email author
  1. 1.FEMTO-ST Institute, CNRS - Univ. Bourgogne Franche-Comté (UBFC)BelfortFrance

Personalised recommendations