Skip to main content
SpringerLink
Log in

Image denoising using DLNN to recognize the direction of pixel variation

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

Abstract

Images are unavoidable to be corrupted by impulse noise, causing the degradation of image quality. A directional-weighted-median (DWM) filter is beneficial to restore the degraded edges. However, the DWM filter only utilizes the local properties to define the weights of the neighboring pixels for noisy image denoising. The pixel relationship among similar patches of the image is ignored, causing the restoration performance no further improvement. In this paper, we apply a deep-learning neural network (DLNN) to determine the pixel-variation direction for noisy image denoising. Because the DLNN is trained by using a noisy image and its clean one as the target image, the pixel-variation relationship of the noisy image and its clean one is considered. Thus, the performance of the DWM filter is able to be improved by the proposed DLNN DWM filter significantly. The experimental results reveal that the proposed DLNN DWM filter can effectively remove interference noise in a noisy image with noise density ranging from 10 to 90%.

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

Similar content being viewed by others

References

  1. Lu, C.T., Chou, T.C.: Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter. Pattern Recognit. Lett. 33(10), 1287–1295 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Dong, Y., Xu, S.: A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Process. Lett. 14(3), 193–196 (2007)

    Article  Google Scholar 

  4. Zhang, X., Xiong, Y.: Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter. IEEE Signal Process. Lett. 16(4), 295–298 (2009)

    Article  Google Scholar 

  5. Toh, K.K.V., Mat-Isa, N.A.: Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Process. Lett. 17(3), 281–284 (2010)

    Article  Google Scholar 

  6. Subrahmanyam, G.R.K.S., Rajagopalan, A.N., Aravind, R.: Importance sampling-based unscented Kalman filter for film-grain noise removal. IEEE Multimedia 15(2), 52–63 (2008)

    Article  Google Scholar 

  7. Wus, J., Tian, Li, J.H., Zhang, D.Q., Jiang, W., Wu, Z., Yu, H.: Toward experiential mobile media processing. IEEE Multimedia 21(2), 80–89 (2014)

    Article  Google Scholar 

  8. Papyan, V., Elad, M.: Muti-scale patch-based image restoration. IEEE Trans. Image Process. 25(1), 249–261 (2016)

    Article  MathSciNet  Google Scholar 

  9. Yue, H., Sun, X., Yang, J., Wu, F.: Image denoising by exploring external and internal correlations. IEEE Trans. Image Process. 24(6), 1967–1982 (2015)

    Article  MathSciNet  Google Scholar 

  10. Garg, B.: Restoration of highly salt-and-pepper-noise-corrupted images using novel adaptive trimmed median filter. Signal Image Video Process. (2020)

  11. Lu, C.T., Chen, Y.Y., Wang, L.L., Chang, C.F.: Removal of salt-and- pepper noise in corrupted image using three-values-weighted approach with variable-size window. Pattern Recognit. Lett. 80, 188–199 (2016)

    Article  Google Scholar 

  12. Sanaee, P., Moallem, P., Razzazi, F.: An interpolation filter based on natural neighbor Galerkin method for salt and pepper noise restoration with adaptive size local filtering window. Signal Image Video Process. 13, 895–903 (2019)

    Article  Google Scholar 

  13. Lu, C.T., Chen, M.Y., Shen, J.H., Wang, L.L., Hsu, C.C.: Removal of salt-and-pepper noise for X-ray bio-images using pixel-variation gain factors. Comput. Electr. Eng. 71, 862–876 (2018)

    Article  Google Scholar 

  14. Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., Lin, C.W.: Deep learning on image denoising: an overview. Neural Networks 131, 251–275 (2020)

    Article  Google Scholar 

  15. Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M.K., Zhang, Y., Sun, L., Wang, G.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)

    Article  Google Scholar 

  16. 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 

  17. Liu, Z., Ya, W.Q., Yang, M.L.: Image denoising based on a CNN model, in Proc, pp. 389–3932. Int. Conf. Control, Automation Robotics (2018)

    Google Scholar 

  18. 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)

    Article  MathSciNet  Google Scholar 

  19. Isogawa, K., Shiodera, T., Takeguchi, T.: Deep shrinkage convolutional neural network for adaptive noise reduction. IEEE Signal Process. Lett. 25(2), 224–228 (2017)

    Article  Google Scholar 

  20. Uddin, A.F.M.S., Chung, T., Bae, S.H.: A perceptually inspired new blind image denoising method using L1 and perceptual loss. IEEE Access 7, 90538–90549 (2019)

    Article  Google Scholar 

  21. Lu, Z., Zhang, Y.: Sparse approximation via penalty decomposition methods. SIAM J. Optimization 23(4), 2448–2478 (2013)

    Article  MathSciNet  Google Scholar 

  22. Yuan, G., Ghanem, B.: l0 TV: a sparse optimization method for impulse noise image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 352–364 (2019)

    Article  Google Scholar 

  23. Chen, R.H., Lu, C.T., Wang, L.L., Lin, C.A., Shen, J.H.: Removal of salt-and-pepper noise using convolutional-neural networks. Proc. Conf. Inform. Technology App. Outlying Islands, 590-597 (2019)

  24. 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Communication, Asia University, Taichung City, 41354, Taiwan, ROC

    Ching-Ta Lu, Hsin-Ju Hsu & Ling-Ling Wang

  2. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City, 40402, Taiwan, ROC

    Ching-Ta Lu

Authors
  1. Ching-Ta Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsin-Ju Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling-Ling Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ching-Ta Lu.

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

Lu, CT., Hsu, HJ. & Wang, LL. Image denoising using DLNN to recognize the direction of pixel variation. SIViP 15, 1247–1256 (2021). https://doi.org/10.1007/s11760-021-01855-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-01855-z

Keywords

Search

Navigation