A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation

Abasnezhad Varzi, Ramzan; Vahidi, Javad; Motameni, Homayun

doi:10.22075/ijnaa.2021.4651

A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation

Document Type : Research Paper

Authors

Ramzan Abasnezhad Varzi ¹

Javad Vahidi ²

Homayun Motameni ³

¹ Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran.

² Department of Mathematics, Iran University of Science and Technology, Tehran 1684613114, Iran. Department of Mathematical Sciences, University of South Africa, Pretoria 0002, South Africa.

³ Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran.

10.22075/ijnaa.2021.4651

Abstract

In this paper, a real-time denoising filter based on the modelling of stable hybrid models is presented. The hybrid models are composed of the shearlet filter and the adaptive Wiener filter in different forms. The optimization of various models is accomplished by the genetic algorithm. Next, regarding the significant relationship between optimal models and input images, changing the structure of optimal models for image denoising is modelled by the ANFIS. The eight hundred digital images are used as train images. For eight hundred training images, sixty-seven models are found. For integrated evaluation, the amounts of image attributes such as Peak Signal Noise Ratio, Signal Noise Ratio, Structural Similarity Index, Mean Absolute Error and Image Quality Assessment are evaluated by the Fuzzy deduction system. Finally, for the features of a sample noisy image as test data, the proposed denoising model of ANFIS is compared with wavelet filter in 2 and 4 levels, Fast bilateral filter, TV-L1, Median, shearlet filter and the adaptive Wiener filter. In addition, the run time of the proposed method is evaluated. Experiments show that the proposed method has better performance than others.

Keywords

Genetic algorithm

denoising

Fuzzy deduction system

image processing

wavelet transformation

adaptive bilateral filters

adaptive neuro-fuzzy inference system

20.1001.1.20086822.2021.12.1.2.7

[1] T. Loganayagi and K. R. Kashwan, A robust edge-preserving bilateral filter for ultrasound kidney image, Indian J. Sci. Technol. 8(23) (2015).

[2] M. Kumar, M. Diwakar, CT image denoising using locally adaptive shrinkage rule in tetrolate Domain, J. King Saud Univ. Comput. Inf. Sci. http:// dx.doi.org /10.1016/ j.jksuci. 2016.03.003.

[3] J. Zhang, G. Lin, L. Wu, C. Wang, and Y. Cheng, Wavelet and fast bilateral filter based de-speckling method for medical ultrasound images, 2015.

[4] M. Diwakar, Sonam, and M. Kumar, CT image denoising based on complex wavelet transform using local adaptive thresholding and bilateral filtering, WCI Proc. Third Int. Symp. Women in Computing and Inf., 2015, pp. 297-302.

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

[6] N.R. Indulekha and M. Sasikumar, Medical image denoising using three dimensional discrete wavelet transform and bilateral filter, Int. J. Manag. Appl. Sci. 1(8) (2015).

[7] S. Sari, S.Z.H. Al Fakkri, H. Roslan, and Z. Tukiran, Development of denoising method for digital image in low-light condition, IEEE Int. Conf. Control Syst. Comput. Engin., Penang, Malaysia, 2013.

[8] N. Chandrakar, Mr. Devanand Bhonsle, A New Hybrid Image Denoising Method, J. Engin. Comput. Appl. Sci. 2(1) (2013).

[9] S. Roy, N. Sinha, and A K. Sen, An efficient denoising model based on wavelet and bilateral filters, Int. J. Comput. Appl. 53(10) (2012).

[10] S.W. Choi, K.W. Nam, K.M. Lim, E.B. Shim, Y.S. Won, H.M. Woo, H.H. Kwak, M.R. Noh, I.Y. Kim, and S.M. Park, Effect of counter-pulsation control of a pulsatile left ventricular assist device on working load variations of the native heart, BioMed Eng OnLine 13 (2014), 35.

[11] D. Zeng, J. Huang, Z. Bian, S. Niu, H. Zhang, Q. Feng, Z. Liang, and J. Ma, A simple low-dose X-Ray CT simulation from high-dose scan, IEEE Trans. Nuclear Sci. 62 (2015), no. 5.

[12] G.R. Easley and D. Labate, Image Processing Using Shearlets. Kutyniok G., Labate D. (eds) Shearlets. Applied and Numerical Harmonic Analysis. Birkhuser Boston, 2012.

[13] E. Ehsaeyan, A new Shearlet hybrid method for image denoising, Iran. J. Electric. Electronic Engin. 12 (2016), 97-97.

[14] L. Pape, K. Giammarco, J.M. Colombi, C.H. Dagli, N.H. Kilicay-Ergin, and G. Rebovich, A fuzzy evaluation method for system of systems meta-architectures, CSER 2013, 245-254.

[15] H. Salehi, J. Vahidi, T. Abdeljawad, A. Khan and S.Y.B. Rad, A SAR image despeckling method based on an extended adaptive Wiener filter and extended guided filter., Remote Sens 12 (2020), 2371.

[16] H. Salehi, J. Vahidi and H. Motameni, A robust hybrid filter based on evolutionary intelligence and fuzzy evaluation, Int. J. Image Graph. 18(4) (2018), 1850023.

[17] D.V.S. Shijin Kumar, Extraction of texture features using GLCM and shape features using connected regions, Int. J. Engin. Technol. 8(6) (2016).