Skip to main content
SpringerLink
Log in

Image denoising via deep residual convolutional neural networks

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Recently, convolutional neural network (CNN)-based methods have achieved impressive performance on image denoising. Notably, CNN with deeper and thinner structures is more flexible to extract the image details. However, direct stacking some existing networks is difficult to achieve satisfactory denoising performance. In this paper, we propose a novel deep residual convolutional neural network (DRCNN) for image denoising. The main structure of DRCNN is the residual block that consists of two convolutional layers, and there are skip connections between these two convolutional layers without the batch normalization operation. The skip connection not only directly transfers the input image information to the hidden layer but also reduces the path length of gradient transfer, making the gradient transfer in a short path and alleviating the vanishing-gradient problem. DRCNN is compared with several state-of-the-art algorithms, and the experimental results demonstrated its denoising effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Abadi, M.: Tensorflow: learning functions at scale. ACM SIGPLAN Not. 51(9), 1–1 (2016)

    Article  Google Scholar 

  2. 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 (2005)

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  5. Dong, C., Chen, C.L., He, K., Tang, X.: Learning a Deep Convolutional Network for Image Super-Resolution. Springer, Berlin (2014)

    Book  Google Scholar 

  6. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)

    Article  MathSciNet  Google Scholar 

  7. Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862–2869 (2014)

  8. Harmeling, S.: Image denoising: Can plain neural networks compete with BM3D? In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392–2399 (2012)

  9. He, K., Zhang, X., Ren, S., Sun, J.: Identity Mappings in Deep Residual Networks. Springer, Berlin (2016)

    Book  Google Scholar 

  10. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)

  11. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR, pp. 1646–1654 (2015)

  12. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: The 3rd International Conference on Learning Representations (ICLR 2015)

  13. Lan, R., He, J., Wang, S., Gu, T., Luo, X.: Integrated chaotic systems for image encryption. Signal Process. 147, 133–145 (2018)

    Article  Google Scholar 

  14. Lan, R., Zhou, Y., Liu, Z., Luo, X.: Prior knowledge-based probabilistic collaborative representation for visual recognition. In: IEEE Transactions on Cybernetics, pp. 1–11 (2018). https://doi.org/10.1109/TCYB.2018.2880290

  15. Lan, R., He, J., Wang, S., Liu, Y., Luo, X.: A parameter-selection-based chaotic system. IEEE Trans. Circuits Syst. II: Express Briefs 66(3), 492–496 (2019)

    Article  Google Scholar 

  16. Lan, R., Lu, H., Zhou, Y., Liu, Z., Luo, X.: An LBP encoding scheme jointly using quaternionic representation and angular information. In: Neural Computing and Applications, pp. 1–7 (2019). https://doi.org/10.1007/s00521-018-03968-y

  17. Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: Computer Vision and Pattern Recognition Workshops, pp. 1132–1140 (2017)

  18. Liu, Y.N., Wang, Y.P., Wang, X.F., Xia, Z., Xu, J.F.: Privacy-preserving raw data collection without a trusted authority for IoT. Comput. Netw. 148, 340–348 (2019)

    Article  Google Scholar 

  19. Malshika Welhenge, A., Taparugssanagorn, A.: Human activity classification using long short-term memory network. Signal Image Video Process. 13(4), 651–656 (2019)

    Article  Google Scholar 

  20. 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)

  21. 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: Proceedings Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001, vol. 2, pp. 416–423 (2002)

  22. Rajwade, A., Rangarajan, A., Banerjee, A.: Image denoising using the higher order singular value decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 849–862 (2013)

    Article  Google Scholar 

  23. 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)

  24. Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. In: International Conference on Machine Learning Deep Learning workshop (2015)

  25. Tabatabaei, S.M., Chalechale, A.: Local binary patterns for noise-tolerant sEMG classification. Signal Image Video Process. 13(3), 491–498 (2019)

    Article  Google Scholar 

  26. Wang, J., Fan, Y., Li, Z., Lei, T.: Texture classification using multi-resolution global and local Gabor features in pyramid space. Signal Image Video Process. 13(1), 163–170 (2019)

    Article  Google Scholar 

  27. Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: International Conference on Neural Information Processing Systems, pp. 341–349 (2012)

  28. 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)

    Article  MathSciNet  Google Scholar 

  29. Zhang, M., Gunturk, B.K.: Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. 17(12), 2324–2333 (2008)

    Article  MathSciNet  Google Scholar 

  30. Zhao, S., Yao, H., Gao, Y., Ji, R., Ding, G.: Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Trans. Multimed. 19(3), 632–645 (2016)

    Article  Google Scholar 

  31. Zhao, S., Ding, G., Gao, Y., Han, J.: Approximating discrete probability distribution of image emotions by multi-modal features fusion. In: IJCAI’17, vol. 1000(1), pp. 4669–4675 (2017)

  32. Zhao, S., Ding, G., Gao, Y., Zhao, X., Tang, Y., Han, J., Yao, H., Huang, Q.: Discrete probability distribution prediction of image emotions with shared sparse learning. In: IEEE Transactions on Affective Computing, pp. 1–1 (2018). https://doi.org/10.1109/TAFFC.2018.2818685

  33. Zhao, S., Gao, Y., Ding, G., Chua, T.: Real-time multimedia social event detection in microblog. IEEE Trans. Cybern. 48(11), 3218–3231 (2018)

    Article  Google Scholar 

  34. Zhao, S., Yao, H., Gao, Y., Ding, G., Chua, T.: Predicting personalized image emotion perceptions in social networks. IEEE Trans. Affect. Comput. 9(4), 526–540 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 61702129, 61772149, 61762028, and U1701267), China Postdoctoral Science Foundation (No. 2018M633047), and Guangxi Science and Technology Project (Nos. AD18216004, AD18281079, and 2018GNSFAA138132).

Author information

Authors and Affiliations

  1. Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, China

    Rushi Lan, Haizhang Zou, Cheng Pang & Yanru Zhong

  2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhenbing Liu

  3. National and Local Joint Engineering Research Center of Satellite Navigation and Location Service, Guilin University of Electronic Technology, Guilin, 541004, China

    Xiaonan Luo

Authors
  1. Rushi Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haizhang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cheng Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanru Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhenbing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaonan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng Pang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lan, R., Zou, H., Pang, C. et al. Image denoising via deep residual convolutional neural networks. SIViP 15, 1–8 (2021). https://doi.org/10.1007/s11760-019-01537-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01537-x

Keywords

Search

Navigation