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

  • Vladislav V. Avramov
  • Eugene V. Rybenkov
  • Nick A. PetrovskyEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1055)

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.

Keywords

Image enhancement Thresholding neural network 2-D non-separable quaternionic filter bank 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Engineering DepartmentBelarusian State University of Informatics and RadioelectronicsMinskBelarus

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