High-performance hardware 3D medical imaging using wavelets in the residue number system

High-performance hardware 3D medical imaging using wavelets in the residue number system

Discrete wavelet transform (DWT) is used in medical imaging to solve many problems. The practical DWT implementation requires a lot of time. We propose parallelizing computations by using the non-positional residue number system (RNS) to improve the performance of medical imaging systems. We use RNS...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Chervyakov, N. I., Червяков, Н. И., Lyakhov, P. A., Ляхов, П. А., Nagornov, N. N., Нагорнов, Н. Н., Valueva, M. V., Валуева, М. В.
Format:	Статья
Sprache:	English
Veröffentlicht:	Institute of Electrical and Electronics Engineers Inc. 2020
Schlagworte:	3D image processing Discrete wavelet transform FPGA Hardware implementation Medical imaging Parallel computing Residue number system (RNS)
Online Zugang:	https://dspace.ncfu.ru/handle/20.500.12258/13667
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Low-bit hardware implementation of DWT for 3D medical images processing
von: Lyakhov, P. A., et al.
Veröffentlicht: (2020)

High-quality 3D medical imaging by wavelet filters with reduced coefficients bit-width
von: Chervyakov, N. I., et al.
Veröffentlicht: (2020)

Analysis of the quantization noise in discrete wavelet transform filters for 3D medical imaging
von: Chervyakov, N. I., et al.
Veröffentlicht: (2020)

RNS-based FPGA accelerators for high-quality 3D medical image wavelet processing using scaled filter coefficients
von: Nagornov, N. N., et al.
Veröffentlicht: (2022)

High-Speed Wavelet Image Processing Using the Winograd Method with Downsampling
von: Lyakhov, P. A., et al.
Veröffentlicht: (2023)

Reducing the Computational Complexity of Image Processing Using Wavelet Transform Based on the Winograd Method
von: Lyakhov, P. A., et al.
Veröffentlicht: (2023)

Quantization noise of multilevel discrete wavelet transform filters in image processing
von: Chervyakov, N. I., et al.
Veröffentlicht: (2019)

Analysis of the quantization noise in discrete wavelet transform filters for image processing
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)

Analysis of the quantization noise in discrete wavelet transform filters for image processing
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)

Method of cleaning hair structures for intellectual image classification of skin neoplasms
von: Lyakhova, U. A., et al.
Veröffentlicht: (2021)

Comparative Analysis of Despeckle Filtering Methods for Ultrasound Images
von: Baboshina, V. A., et al.
Veröffentlicht: (2023)

Implementation of Smoothing Image Filtering in the Residue Number System
von: Chervyakov, N. I., et al.
Veröffentlicht: (2019)

High Performance Parallel Pseudorandom Number Generator on Cellular Automata
von: Lyakhov, P. A., et al.
Veröffentlicht: (2022)

Method for determining skin lesions from images using neural network
von: Lyakhova, U. A., et al.
Veröffentlicht: (2020)

Residue number system-based solution for reducing the hardware cost of a convolutional neural network
von: Chervyakov, N. I., et al.
Veröffentlicht: (2020)

Control of the latent image formed as a combination of variously oriented textures
von: Zharkih, A. A., et al.
Veröffentlicht: (2019)

Area-efficient FPGA implementation of minimalistic convolutional neural network using residue number system
von: Chervyakov, N. I., et al.
Veröffentlicht: (2019)

Hardware implementation of video processing device using residue number system
von: Chervyakov, N. I., et al.
Veröffentlicht: (2019)

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
von: Valueva, M. V., et al.
Veröffentlicht: (2020)

High-speed smoothing filter in the residue number system
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)

High-performance digital image filtering architectures in the residue number system based on the Winograd method
von: Valueva, M. V., et al.
Veröffentlicht: (2022)

Increasing of convolutional neural network performance using residue number system
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)

Hardware implementation of a convolutional neural network using calculations in the residue number system
von: Chervyakov, N. I., et al.
Veröffentlicht: (2019)

RNS-based image processing
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)

RNS-based image processing
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)

Analysis of the quantization noise of linear time-invariant filters for image processing
von: Lyakhov, P. A., et al.
Veröffentlicht: (2019)

Classification of moduli sets for residue number system with special diagonal functions
von: Chervyakov, N. I., et al.
Veröffentlicht: (2020)

Two methods of adaptive median filtering of impulse noise in images
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)

How country image affects intention to visit a destination: Evidence from russian tourists visiting switzerland
von: Belozerova, O. I., et al.
Veröffentlicht: (2019)

Optimization of neural network training for image recognition based on trigonometric polynomial approximation
von: Vershkov, N. A., et al.
Veröffentlicht: (2022)

Comparative Analysis of Various Methods to Circuit Design for DWT with CDF 9/7 Wavelet
von: Nagornov, N. N., et al.
Veröffentlicht: (2022)

Single image super-resolution method based on bilinear interpolation and U-Net combination
von: Lyakhov, P. A., et al.
Veröffentlicht: (2021)

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity
von: Lyakhov, P. A., et al.
Veröffentlicht: (2023)

High performance parallel computing in residue number system
von: Deryabin, M. A., et al.
Veröffentlicht: (2018)

Английский язык в сфере культурологии учебное пособие
von: Бардинская, Т. Р.

A new method for adaptive median filtering of images
von: Lyakhov, P. A., et al.
Veröffentlicht: (2019)

User interface design for a web-based image processing and analysis system
von: Rechitskiy, A. S., et al.
Veröffentlicht: (2018)

Image recognition by a second-order convolutional neural network with dynamic receptive fields
von: Nemkov, R. M., et al.
Veröffentlicht: (2018)

System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing
von: Lyakhova, U. A., et al.
Veröffentlicht: (2021)

Efficiency analysis of the image impulse noise cleaning using median filters with weighted central element
von: Chervyakov, N. I., et al.
Veröffentlicht: (2018)