Skip to main content
SpringerLink
Log in

Adaptive neuro fuzzy estimation of the most influential speckle noise distributions in color images for denoising performance prediction

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

Abstract

This research paper analyzes the speckle noise distributions in images for denoising performance prediction through the prism of spatial domain. The values at the maximum, minimum and middle of spectrum in spatial domain are taken as reference values. All obtained results give a better overview of the “nature” of the digital images in comparison to the theoretical definitions of noises and images as digital signals. Therefore, analyses of the noises in the 2D spectrum give good recommendations for improvement of the filters. The main aim in this study is to investigate which speckle noise distributions in images has the strongest influence for denoising performance prediction. The clean images are available and we adopt it for evaluating our network. In our experiments, Peak Signal to Noise Ratio (PSNR), normalized color difference (NCD), and feature similarity index for color image quality assessment (FSIMc), are used to measure denoising performance. is selected as the evaluation index of the image. Studies on speckle noise distributions in images show that such distribution do have certain disciplines. ALOHA filter is the most influential for the denoising performance prediction.

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

Data availability

Data is available on request from corresponding author.

Code availability

Not applicable.

References

  1. Alessandro F, Mejdi T, Vladimir K, Karen E (2008) Practical poissonian-gaussian noise modeling and fitting for single image raw-data. IEEE Trans Image Process 17(10):1737–1754. https://doi.org/10.1109/TIP.2008.2001399

    Article  MathSciNet  MATH  Google Scholar 

  2. Bovik (2000) Handbook of image and video processing. Academic, New York

    MATH  Google Scholar 

  3. Budevski E, Obretenov W, Bostanov W, Staikov G, Doneit J, Jüttner K, Lorenz WJ (1989) Noise analysis in metal deposition-expectations and limits. Electrochim Acta 34(8):1023–1029

  4. Chowdhury D, Gupta S, Roy D, Sarkar D, Chattopadhyay CC, Das SK (2017) A quantum study on digital image noises and their in-depth clusterization. 4th International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India 2–3 Nov. 2017. IEEE Xplore: 30 April 2018. https://doi.org/10.1109/OPTRONIX.2017.8349973

  5. Coupe P, Hellier P, Kervrann C, Barillot C (2009) Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process 18:2221–2229

    Article  MathSciNet  MATH  Google Scholar 

  6. Edward RD (1998) Random processes for image and signal processing: probability theory. Wiley-IEEE Press, Denver

    Google Scholar 

  7. Esmaeilpour M, Mohammadi ARA (2016) Analyzing the EEG signals in order to estimate the depth of anesthesia using wavelet and fuzzy neural networks. Int J Interact Multimed Artif Intell 4(Regular Issue):12–15. https://doi.org/10.9781/ijimai.2016.422

    Article  Google Scholar 

  8. Espinosa LAD, Rosales F, Posadas A (2018) Embedding spatial variability in rainfall field reconstruction. Int J Remote Sens 39(9):2884–2905. https://doi.org/10.1080/01431161.2018.1433894

    Article  Google Scholar 

  9. Farhane N, Boumhidi I, Boumhidi J (2017) Smart Algorithms to control a variable speed wind turbine. Int J Interact Multimed Artif Intell 4(Regular Issue):88–95. https://doi.org/10.9781/ijimai.2017.08.001

    Article  Google Scholar 

  10. Florian L, Thierry B, Michael U (2011) Image denoising in mixed Poisson–Gaussian noise. IEEE Trans Image Process 20(3):696–708. https://doi.org/10.1109/TIP.2010.2073477

    Article  MathSciNet  MATH  Google Scholar 

  11. Frost VS, Stiles JA, Shanmugan KS, Holtzman JC (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell 4:157–166

    Article  Google Scholar 

  12. Ghulyani M, Arigovindan M (2018) Fast total variation based image restoration under mixed Poisson-Gaussian noise model. IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA 4–7 April 2018, IEEE Xplore: 24 May 2018. https://doi.org/10.1109/ISBI.2018.8363801

  13. Gonzalez RC, Woods RE (2001) Digital Image Processing, 2nd edn. Prentice-Hall, New York

    Google Scholar 

  14. Hou L, Liu H, Luo Z, Zhou Y, Truong T-K (2017) Image deblurring in the presence of salt-and-pepper noise. IEEE International Conference on Image Processing (ICIP), Beijing, China 17–20 Sept. 2017, IEEE Xplore: 22 February 2018. https://doi.org/10.1109/ICIP.2017.8296710

  15. Jang J-SR (1993) Adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  16. Jong-Sen L (1980) Digital image enhancement and noise filtering by Use of local statistics. IEEE Trans Pattern Anal Mach Intell 2(2):165–168

    Google Scholar 

  17. Keh-Shih C, Hong-Long T, Sharon C, Jay W, Tzong-Jer C (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15

    Article  Google Scholar 

  18. Le T, Chartrand R, Asaki TJ (2007) A variational approach to reconstructing images corrupted by Poisson noise. J Math Imaging Vis 27(3):257–263. https://doi.org/10.1007/s10851-007-0652-y

    Article  MathSciNet  Google Scholar 

  19. Lee JS (2009) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell 2:165–168

    Google Scholar 

  20. Lucas J, Van V, Ian TY, Guus LB (1989) A nonlinear Laplace operator as edge detector in noisy images. Comput Vis Graphics Image Process 45(2):167–195

    Article  Google Scholar 

  21. N. ABSoft (2017) Neat image, in. Accessed: May 2017

  22. Parrilli S, Poderico M, Angelino CV, Verdoliva L (2012) A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans Geoence Remote Sens 50:606–616

    Article  Google Scholar 

  23. Portilla J, Strela V, Wainwright MJ, Simoncelli EP (2003) Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans Image Process 12(11):1338–1351. https://doi.org/10.1109/TIP.2003.818640

    Article  MathSciNet  MATH  Google Scholar 

  24. Vijaykumar VR, Vanathi PT, Kanagasabapathy P, Ebenezer D (2008) High density impulse noise removal using robust estimation based Filter. IAENG - International Journal of Computer Science 35(3):259–266

    Google Scholar 

  25. Wang Z, Zhang D (1999) Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans Circuits Syst II 46(1):78–80. https://doi.org/10.1109/82.749102

    Article  Google Scholar 

  26. Yu Y, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11:1260–1270

    Article  MathSciNet  Google Scholar 

  27. Yue H, Liu J, Yang J, Nguyen T, Hou C (2017) Image noise estimation and removal considering the bayer pattern of noise variance. IEEE International Conference on Image Processing (ICIP), Beijing, China 17–20 Sept. 2017, IEEE Xplore: 22 February 2018. https://doi.org/10.1109/ICIP.2017.8296828

  28. Zhang K, Zuo WM, Chen YJ, Meng DY, Zhang L (2017) Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26:3142–3155

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhang W, Jin L, Song E, Xu X (2019) Removal of impulse noise in color images based on convolutional neural network. Appl Soft Comput 82:105558

    Article  Google Scholar 

  30. Zhu Y, Li Y, Gong H, Luo Q, Li A (2017) Three-dimensional bilateral filtering applying to DE-noise of microscopic image stack. 2nd IET International Conference on Biomedical Image and Signal Processing (ICBISP 2017), Wuhan, China 13–14 May 2017, IEEE Xplore: 14 December 2017. https://doi.org/10.1049/cp.2017.0083

Download references

Author information

Authors and Affiliations

  1. Faculty of Natural Sciences in Kosovska Mitrovica, University of Pristina, Kosovska Mitrovica, Serbia

    Nebojša Denić, Zoran Nešić, Ivana Ilić, Dragan Zlatković & Jelena Stojanović

  2. Faculty of Mathematics and Computer Sciences, Аlfa BK University, Belgrade, Belgrade, Serbia

    Bojan Stojiljković

  3. Pedagogical Faculty in Vranje, University of Niš, Partizanska 14, 17500, Vranje, Serbia

    Dalibor Petkovic

Authors
  1. Nebojša Denić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zoran Nešić
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivana Ilić
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dragan Zlatković
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bojan Stojiljković
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jelena Stojanović
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dalibor Petkovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Nebojša Denić did simulation, Zoran Nešić did analysis, Ivana Ilić did measurement, Dragan Zlatković did simulation, Bojan Stojiljković did measurement, Jelena Stojanović did siulation, Dalibor Petković did discussion.

Corresponding author

Correspondence to Dalibor Petkovic.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflicts of interest/Competing interests

Not applicable.

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

Denić, N., Nešić, Z., Ilić, I. et al. Adaptive neuro fuzzy estimation of the most influential speckle noise distributions in color images for denoising performance prediction. Multimed Tools Appl 82, 21729–21742 (2023). https://doi.org/10.1007/s11042-023-14633-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14633-5

Keywords

Search

Navigation