| DC Field | Value | Language |
|---|
| dc.contributor.author | Avramov, V. V. | - |
| dc.contributor.author | Rybenkov, E. V. | - |
| dc.contributor.author | Petrovsky, N. A. | - |
| dc.date.accessioned | 2020-12-14T12:11:10Z | - |
| dc.date.available | 2020-12-14T12:11:10Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Avramov, V. V. Thresholding Neural Network Image Enhancement Based on 2-D Non-separable Quaternionic Filter Bank / V. V. Avramov, E. V. Rybenkov, N. A. Petrovsky // Pattern Recognition and Information Processing : 14th International conference, Minsk, 21–23 may 2019 / Springer. – Minsk, 2019. – P. 147–161. – DOI: https://doi.org/10.1007/978-3-030-35430-5_13. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/41685 | - |
| dc.description.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”, CG2D=17,15dB , prototype filter bank ( 8×24 ) 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 1dB – 1.5dB higher. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | Springer | ru_RU |
| dc.subject | авторефераты диссертаций | ru_RU |
| dc.subject | image enhancement | ru_RU |
| dc.subject | thresholding neural network | ru_RU |
| dc.title | Thresholding Neural Network Image Enhancement Based on 2-D Non-separable Quaternionic Filter Bank | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | Публикации в зарубежных изданиях
|
