Skip to main content
SpringerLink
Log in

Multiclass CNN-based adaptive optimized filter for removal of impulse noise from digital images

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Multiclass CNN-based window adaptive optimized filter has been proposed in this research work to remove impulse noise from colored images. Instead of applying adaptive filter on the whole images, selective adaptive window has been applied on the test image so that the effective computational complexity of the system is reduced. In this article, multiclass CNN has been used for the choosing the range of selective window. After that, vectored minimum mean value-based detection is being applied on the pixel under operation on a particular kernel of image for detection of noise and defining the appropriate size of the window surrounding the pixel under operation. An adaptive vector median filter incorporated with particle swarm optimization (AVMPSO) is passed over the corrupted pixel if the pixel is determined as corrupted after detection of noisy and noise-free pixels. The performance comparison of the proposed AVMPSO has been carried out along with the available state-of-the-art filters in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index matrix (SSIM), and feature similarity index matrix (FSIMC). A large set of images has been considered to conclude how AVMPSO outperforms the existing methods at all levels of noise by ~ 1.5 to 3 dB (PSNR) at least.

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
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Pitas, I., Venetsanopoulos, A.N.: Order statistics in digital image processing. Proc. IEEE 80(12), 1893–1921 (1992)

    Article  Google Scholar 

  2. Tukey, J.W.: Exploratory Data Analysis (1977)

  3. Tukey, J.W.: Nonlinear (Nonsuperposable) Methods for Smoothing Data. In: Congressional Record 1974 EASCON, 673 (1974)

  4. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, Nueva Jersey (2008)

  5. Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proc. IEEE 78(4), 678–689 (1990)

    Article  Google Scholar 

  6. Sun, T., Neuvo, Y.: Detail-preserving median based filters in image processing. Pattern Recogn. Lett. 15(4), 341–347 (1994)

    Article  Google Scholar 

  7. Ko, S.J., Lee, Y.H.: Center weighted median filters and their applications to image enhancement. IEEE Trans. Circuits Syst. 38(9), 984–993 (1991)

    Article  Google Scholar 

  8. Alparone, L., Baronti, S., Carla, R.: Two-dimensional rank-conditioned median filter. IEEE Trans. Circuits Syst. Analog Digital Signal Process, 42(2), 130–132 (1995)

  9. Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Circuits Syst. II Analog Digital Signal Process. 46(1), 78–80 (1999)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Geng, X., Hu, X., Xiao, J.: Quaternion switching filter for impulse noise reduction in color image. Signal Process. 92(1), 150–162 (2012)

    Article  Google Scholar 

  12. Horng, S.J., Hsu, L.Y., Li, T., Qiao, S., Gong, X., Chou, H.H., Khan, M.K.: Using sorted switching median filter to remove high-density impulse noises. J. Vis. Commun. Image Represent. 24(7), 956–967 (2013)

    Article  Google Scholar 

  13. Laskar, R.H., Bhowmick, B., Biswas, R., Kar, S.: Removal of impulse noise from color image. In: TENCON 2009–2009 IEEE Region 10 Conference (pp. 1–5), IEEE (2009)

  14. Eng, H.L., Ma, K.K.: Noise adaptive soft-switching median filter. IEEE Trans. Image Process. 10(2), 242–251 (2001)

    Article  MATH  Google Scholar 

  15. Singh, K.M., Bora, P.K.: Adaptive vector median filter for removal impulses from color images. In: Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS'03, vol. 2, IEEE, pp. II-396 (2003)

  16. Roy, A., Laskar, R.H.: Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images. AEU-Int. J. Electron. C. 72, 114–124 (2017)

    Article  Google Scholar 

  17. Mohapatra, S., Sa, P.K., Majhi, B.: Adaptive threshold selection for impulsive noise detection in images using coefficient of variance. Neural Comput. Appl. 21(2), 281–288 (2012)

    Article  Google Scholar 

  18. Smolka, B., Lukac, R., Chydzinski, A., Plataniotis, K.N., Wojciechowski, W.: Fast adaptive similarity based impulsive noise reduction filter. Real Time Imaging 9(4), 261–276 (2003)

    Article  Google Scholar 

  19. Yuan, S.Q., Tan, Y.H.: Impulse noise removal by a global–local noise detector and adaptive median filter. Signal Process. 86(8), 2123–2128 (2006)

    Article  MATH  Google Scholar 

  20. Lin, C.H., Tsai, J.S., Chiu, C.T.: Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Trans. Image Process. 19(9), 2307–2320 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  21. Jafar, I.F., AlNa’mneh, R.A., Darabkh, K.A.: Efficient improvements on the BDND filtering algorithm for the removal of high-density impulse noise. IEEE Trans. Image Process. 22(3), 1223–1232 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  22. Dawood, H., Dawood, H., Guo, P.: Removal of random-valued impulse noise by local statistics. Multimedia Tools Appl. 74(24), 11485–11498 (2015)

    Article  Google Scholar 

  23. Esakkirajan, S., Veerakumar, T., Subramanyam, A.N., PremChand, C.H.: Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process. Lett. 18(5), 287–290 (2011)

    Article  Google Scholar 

  24. Ahmed, F., Das, S.: Removal of high-density salt-and-pepper noise in images with an iterative adaptive fuzzy filter using alpha-trimmed mean. IEEE Trans. Fuzzy Syst. 22(5), 1352–1358 (2014)

    Article  Google Scholar 

  25. Bhadouria, V.S., Ghoshal, D., Siddiqi, A.H.: A new approach for high density saturated impulse noise removal using decision-based coupled window median filter. SIViP 8(1), 71–84 (2014)

    Article  Google Scholar 

  26. Schulte, S., De Witte, V., Nachtegael, M., Van der Weken, D., Kerre, E.E.: Histogram-based fuzzy colour filter for image restoration. Image Vis. Comput. 25(9), 1377–1390 (2007)

    Article  Google Scholar 

  27. Masood, S., Hussain, A., Jaffar, M.A., Choi, T.S.: Color differences based fuzzy filter for extremely corrupted color images. Appl. Soft Comput. 21, 107–118 (2014)

    Article  Google Scholar 

  28. Verma, O.P., Hanmandlu, M., Sultania, A.K., Parihar, A.S.: A novel fuzzy system for edge detection in noisy image using bacterial foraging. Multidimens. Syst. Signal Process. 24(1), 1–18 (2012)

    MathSciNet  MATH  Google Scholar 

  29. Budak, C., Türk, M., Toprak, A.: Reduction in impulse noise in digital images through a new adaptive artificial neural network model. Neural Comput. Appl. 26(4), 835–843 (2015)

    Article  Google Scholar 

  30. Roy, A., Laskar, R.H.: Impulse noise removal based on SVM classification. In: TENCON 2015–2015 IEEE Region 10 Conference, IEEE, pp. 1–5 (2015)

  31. Roy, A., Singha, J., Devi, S.S., Laskar, R.H.: Impulse noise removal using SVM classification based fuzzy filter from gray scale images. Signal Process. 128, 262–273 (2016)

    Article  Google Scholar 

  32. Roy, A., Laskar, R.H.: Multiclass SVM based adaptive filter for removal of high density impulse noise from color images. Appl. Soft Comput. 46, 816–826 (2015)

    Article  Google Scholar 

  33. Roy, A., Devi, S.S., Laskar, R.H.: Impulse noise removal from gray scale images based on ANN classification based Fuzzy filter. In: CINE 2016 conference, IEEE, pp. 1–5 (2016)

  34. Mursal, A.S.N., Ibrahim, H.: Median filtering using first-order and second-order neighborhood pixels to reduce fixed value impulse noise from grayscale digital images. Electronics 9(12), 2034 (2020)

    Article  Google Scholar 

  35. Veerakumar, T., Subudhi, B.N., Esakkirajan, S.: Empirical mode decomposition and adaptive bilateral filter approach for impulse noise removal. Expert Syst. Appl. 121, 18–27 (2019)

    Article  Google Scholar 

  36. Chan, R.H., Ho, C.W., Nikolova, M.: Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans. Image Process. 14(10), 1479–1485 (2005)

    Article  Google Scholar 

  37. Singh, K.M.: Vector median filter based on non-causal linear prediction for detection of impulse noise from images. Int. J. Comput. Sci. Eng. 7(4), 345–355 (2012)

    Google Scholar 

  38. 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 

  39. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  40. Abdelmounaime, S., Dong-Chen, H.: New Brodatz-based image databases for grayscale color and multiband texture analysis. Int. Sch. Res. Not. 2013, 14 (2013)

    Google Scholar 

  41. Liu, S., Fan, J., Ai, D., Song, H., Fu, T., Wang, Y., Yang, J.: Feature matching for texture-less endoscopy images via superpixel vector field consistency. Biomed. Opt. Express 13(4), 2247–2265 (2022)

    Article  Google Scholar 

Download references

Funding

This study was funded by no funding agency.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Ghani Khan Institute of Engineering and Technology, Malda, India

    Amarjit Roy

  2. School of Electronics, VIT-AP University, Vijayawada, India

    Lakhan Dev Sharma

  3. Department of Computer Science Engineering, Thapar Institute of Engineering and Technology Patiala, Punjab, India

    Alok Kumar Shukla

Authors
  1. Amarjit Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lakhan Dev Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alok Kumar Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amarjit Roy.

Ethics declarations

Conflict of interest

Dr. Amarjit Roy has no conflict of interest. Dr. Lakhan Dev Sharma has no conflict of interest. Dr. Alok Kumar Shukla has no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, A., Sharma, L.D. & Shukla, A.K. Multiclass CNN-based adaptive optimized filter for removal of impulse noise from digital images. Vis Comput 39, 5809–5822 (2023). https://doi.org/10.1007/s00371-022-02697-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-022-02697-7

Keywords

Search

Navigation