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2019 | OriginalPaper | Chapter

Thresholding Neural Network Image Enhancement Based on 2-D Non-separable Quaternionic Filter Bank

Authors : Vladislav V. Avramov, Eugene V. Rybenkov, Nick A. Petrovsky

Published in: Pattern Recognition and Information Processing

Publisher: Springer International Publishing

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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.

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Title: Thresholding Neural Network Image Enhancement Based on 2-D Non-separable Quaternionic Filter Bank
Authors: Vladislav V. Avramov
Eugene V. Rybenkov
Nick A. Petrovsky
Publisher: Springer International Publishing
Book: Pattern Recognition and Information Processing
Print ISBN: 978-3-030-35429-9

Electronic ISBN: 978-3-030-35430-5

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-35430-5_13

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