Skip to main content
SpringerLink
Log in

A convolutional neural networks denoising approach for salt and pepper noise

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.

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

Similar content being viewed by others

Notes

  1. Here, we empirically set δ to 1.

  2. https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/BSDS300/html/dataset/images.html

  3. https://github.com/gkh178/noise-adaptive-switching-non-local-means/tree/master/NASNLM

  4. http://www.ipol.im/pub/art/2016/161/

  5. https://github.com/urbste/MLPnP_matlab_toolbox

References

  1. Aiswarya K, Jayaraj V, Ebenezer D (2010) A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos. Int Conf Comput Model Simul 2010, vol. 4, pp. 409-413. IEEE

  2. Astola J, Kuosmanen P (1997) Fundamentals of nonlinear digital filtering, electronic engineering systems. CRC-Press, Boca Raton

    Google Scholar 

  3. Buades, A., Coll, B., Morel, J. M.: A non-local algorithm for image denoising. In: IEEE Comput Soc Conf Comput Vision Pattern Recog 2005 vol. 2, pp. 60-65. IEEE (2005)

  4. Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095

    Article  MathSciNet  Google Scholar 

  5. Delon J, Desolneux A, Guillemot T (2016) PARIGI: a patch-based approach to remove impulse-Gaussian noise from images. Image Process On Line 5:130–154

    Article  MathSciNet  Google Scholar 

  6. Dong C, Change C, Loy C, He KM, Tang XO (2016) Learning a deep convolutional network for image super-resolution. IEEE Conf Comput Vision Pattern Recogn

  7. Eng HL, Ma KK (2001) Noise adaptive soft-switching median filter. IEEE Trans Image Process 10(2):242–251

    Article  Google Scholar 

  8. Esakkirajan S, Veerakumar T, Subramanyam AN, Premchand CH (2011) Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Sign Process Lett 18(5):287–290

    Article  Google Scholar 

  9. Fu B, Li WW, Fu YP, Song CM (2015) An image topic model for image denoising. Neurocomputing 169:119–123

    Article  Google Scholar 

  10. Ghifary M, Kleijn WB, Zhang M, Balduzzi D, Li W (2016) Deep reconstruction-classification networks for unsupervised domain adaptation (DRCN). Eur Conf Comput Vision (ECCV)

  11. Harmeling S, Schuler CJ, Burger HC (2012) Image denoising: can plain neural networks compete with BM3D? IEEE Conf Comput Vision Pattern Recogn 2012 157:2392–2399 IEEE Computer Society

    Google Scholar 

  12. Hwang H, Haddad RA (1995) Adaptive median filter: new algorithms and results. IEEE Trans Image Process 4(4):499–502

    Article  Google Scholar 

  13. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Int Conf Mach Learn 2015: 448–456

  14. Jafar IF, Alna'Mneh RA, Darabkh KA (2013) Efficient improvements on the BDND filtering algorithm for the removal of high-density impulse noise. IEEE Trans Image Process 22(3):1223–1232

    Article  MathSciNet  Google Scholar 

  15. Jain V, Seung HS (2008) Natural image denoising with convolutional networks. Int Conf Neural Inform Process Systems 2008, 769–776. Curran Associates Inc

  16. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks, IEEE Conf Comput Vision Pattern Recogn

  17. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Int Conf Neural Inform Process Syst 2012, vol. 60, pp. 1097-1105. Curran Associates Inc

  18. Marco Bevilacqua CG, Roumy A, Morel M-LA (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. BMVC 2:5–7

    Google Scholar 

  19. Ng PE, Ma KK (2006) A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans Image Process 15(6):1506–1516

    Article  Google Scholar 

  20. Toh KKV, Ibrahim H, Mahyuddin MN (2008) Salt-and-pepper noise detection and reduction using fuzzy switching median filter. IEEE Trans Consum Electron 54(4):1956–1961

    Article  Google Scholar 

  21. Varghese J, Tairan N, Subash S (2015) Adaptive switching non-local filter for the restoration of salt and pepper impulse-corrupted digital images. Arab J Sci Eng 40(11):3233–3246

    Article  Google Scholar 

  22. Wang W, Lu P (2011) An efficient switching median filter based on local outlier factor. IEEE Sign Process Lett 18(10):551–554

    Article  Google Scholar 

  23. Wang Y, Lin X, Wu L, Zhang W, Zhang Q, Huang X (2015) Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans Image Process 24(11):3939–3949

    Article  MathSciNet  Google Scholar 

  24. Wang Y, Lin X, Wu L, Zhang W (2015) Effective multi-query expansions: robust landmark retrieval, ACM Multimed:79–88

  25. Wang Y, Lin X, Wu L, Zhang W, Zhang Q (2015) LBMCH: learning bridging mapping for cross-modal hashing. ACM SIGIR

  26. Wang Y, Zhang W, Wu L, Lin X, Fang M, Pan S (2016) Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering. IJCAI : 2153–2159

  27. Wang Y, Lin X, Wu L, Zhang W (2017) Effective multi-query expansions: Collborative deep networks for robust landmark retrieval. IEEE Trans Image Process 26(3):1393–1404

    Article  MathSciNet  Google Scholar 

  28. Wang Y, Zhang W, Wu L, Lin X, Zhao X (2017) Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion. IEEE Trans Neural Netw Learn Syst 28(1):57–70

    Article  Google Scholar 

  29. Wang Y, Wu L, Lin X, Gao J (2018) Multiview spectral clustering via structured low-rank matrix factorization. IEEE Trans Neural Netw Learn Syst

  30. Wu L (2018) Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Netw 103:1–8

    Article  Google Scholar 

  31. Wu L, Wang Y (2017) Robust hashing for multi-view data: jointly learning low-rank Kernelized similarity consensus and hash functions. Image Vis Comput 57:58–66

    Article  Google Scholar 

  32. Wu L, Wang Y, Li X, Gao J (2018) Deep attention-based spatially recursive networks for fine-grained visual recognition. IEEE Trans Cybernet

  33. Wu L, Wang Y, Gao J, Li X (2018) Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn 73:275–288

    Article  Google Scholar 

  34. Wu L, Wang Y, Li X, Gao J (2018) What-and-where to match: deep spatially multiplicative integration networks for person re-identification. Pattern Recogn 76:727–738

    Article  Google Scholar 

  35. Wu L, Wang Y, Shao L (2018) Cycle-consistent deep generative hashing for cross-modal retrieval. arXiv:1804.11013,

  36. Xie J, Xu L, Chen E (2012) Image denoising and inpainting with deep neural networks. Adv Neural Inf Proces Syst 1:314–349

    Google Scholar 

  37. Yang J, Wright, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 2:7

    MathSciNet  MATH  Google Scholar 

  38. Zhang S, Karim MA (2002) A new impulse detector for switching median filters. Sign Process Lett IEEE 9(11):360–363

    Article  Google Scholar 

  39. Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian Denoiser: residual learning of deep CNN for image Denoising. IEEE Trans Image Process 26(7):3142–3155

    Article  MathSciNet  Google Scholar 

  40. Zhou YY, Ye ZF, Huang JJ (2012) Improved decision-based detail-preserving variational method for removal of random-valued impulse noise. Image Process Iet 6(7):976–985

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) No. 61702246, Liaoning Province of China General Project of Scientific Research No. L2015285, Liaoning Province of China Doctoral Research Start-Up Fund No. 201601243.

Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, China

    Bo Fu, Xiaoyang Zhao, Yi Li, Xianghai Wang & Yonggong Ren

Authors
  1. Bo Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianghai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yonggong Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Fu.

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

Fu, B., Zhao, X., Li, Y. et al. A convolutional neural networks denoising approach for salt and pepper noise. Multimed Tools Appl 78, 30707–30721 (2019). https://doi.org/10.1007/s11042-018-6521-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6521-4

Keywords

Search

Navigation