Abstract
The thresholding neural network with a 2-D non-separable paraunitary filter bank based on quaternion multipliers (2-D NSQ-PUFB) for image enhancement is proposed. Due to the high characteristics of the multi-bands 2-D NSQ-PUFB (structure “64in-64out”, \(CG_{2D} = {{17,15\,\mathrm{\text {dB}}}}\), prototype filter bank (\( 8 \times 24 \)) Q-PUFB), which forms the basis of the TNN, the results of noise editing in comparison with the approaches based on the two-channel wavelet transform in terms of PSNR are \({{1\,\mathrm{\text {dB}}}}\)–\({{1.5\,\mathrm{\text {dB}}}}\) higher.
Supported by Belarusian Republican Foundation for Fundamental Research (project no. F18MV-016).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bhutada, G., Anand, R., Saxena, S.: Image enhancement by wavelet-based thresholding neural network with adaptive learning rate. IET Image Process. 5(7), 573 (2011). https://doi.org/10.1049/iet-ipr.2010.0014
Krim, H., Tucker, D., Mallat, S., Donoho, D.: On denoising and best signal representation. IEEE Trans. Inf. Theory 45(7), 2225–2238 (1999). https://doi.org/10.1109/18.796365
Nasri, M., Nezamabadi-pour, H.: Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing 72(4–6), 1012–1025 (2009). https://doi.org/10.1016/j.neucom.2008.04.016
Parfieniuk, M., Petrovsky, A.: Inherently lossless structures for eight- and six-channel linear-phase paraunitary filter banks based on quaternion multipliers. Signal Process. 90, 1755–1767 (2010). https://doi.org/10.1016/j.sigpro.2010.01.008
Petrovsky, N.A., Rybenkov, E.V.: 2-D non-separable integer implementation of paraunitary filter bank based on the quaternionic multiplier block-lifting structure. In: 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, September 2019
Petrovsky, N.A., Rybenkov, E.V., Petrovsky, A.A.: Two-dimensional non-separable quaternionic paraunitary filter banks. In: 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, September 2018. https://doi.org/10.23919/spa.2018.8563311
Sattar, F., Floreby, L., Salomonsson, G., Lovstrom, B.: Image enhancement based on a nonlinear multiscale method. IEEE Trans. Image Process. 6(6), 888–895 (1997). https://doi.org/10.1109/83.585239
Vaidyanathan, P.P.: Multirate Systems and Filter Banks. Prentice Hall, Englewood Cliffs (1992)
Yu, H., Zhao, L., Wang, H.: Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain. IEEE Trans. Image Process. 18(10), 2364–2369 (2009). https://doi.org/10.1109/tip.2009.2026685
Zhang, X.P.: Thresholding neural network for adaptive noise reduction. IEEE Trans. Neural Netw. 12(3), 567–584 (2001). https://doi.org/10.1109/72.925559
Zhang, X.P., Desai, M.: Adaptive denoising based on SURE risk. IEEE Signal Process. Lett. 5(10), 265–267 (1998). https://doi.org/10.1109/97.720560
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Avramov, V.V., Rybenkov, E.V., Petrovsky, N.A. (2019). Thresholding Neural Network Image Enhancement Based on 2-D Non-separable Quaternionic Filter Bank. In: Ablameyko, S., Krasnoproshin, V., Lukashevich, M. (eds) Pattern Recognition and Information Processing. PRIP 2019. Communications in Computer and Information Science, vol 1055. Springer, Cham. https://doi.org/10.1007/978-3-030-35430-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-35430-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35429-9
Online ISBN: 978-3-030-35430-5
eBook Packages: Computer ScienceComputer Science (R0)