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...

Сохранить в:

書目詳細資料
Главные авторы:	Chervyakov, N. I., Червяков, Н. И., Lyakhov, P. A., Ляхов, П. А., Nagornov, N. N., Нагорнов, Н. Н., Valueva, M. V., Валуева, М. В.
格式:	Статья
語言:	English
出版:	Institute of Electrical and Electronics Engineers Inc. 2020
主題:	3D image processing Discrete wavelet transform FPGA Hardware implementation Medical imaging Parallel computing Residue number system (RNS)
在線閱讀:	https://dspace.ncfu.ru/handle/20.500.12258/13667
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

Low-bit hardware implementation of DWT for 3D medical images processing
由: Lyakhov, P. A., и др.
出版: (2020)

High-quality 3D medical imaging by wavelet filters with reduced coefficients bit-width
由: Chervyakov, N. I., и др.
出版: (2020)

Analysis of the quantization noise in discrete wavelet transform filters for 3D medical imaging
由: Chervyakov, N. I., и др.
出版: (2020)

RNS-based FPGA accelerators for high-quality 3D medical image wavelet processing using scaled filter coefficients
由: Nagornov, N. N., и др.
出版: (2022)

High-Speed Wavelet Image Processing Using the Winograd Method with Downsampling
由: Lyakhov, P. A., и др.
出版: (2023)

Reducing the Computational Complexity of Image Processing Using Wavelet Transform Based on the Winograd Method
由: Lyakhov, P. A., и др.
出版: (2023)

Quantization noise of multilevel discrete wavelet transform filters in image processing
由: Chervyakov, N. I., и др.
出版: (2019)

Analysis of the quantization noise in discrete wavelet transform filters for image processing
由: Chervyakov, N. I., и др.
出版: (2018)

Analysis of the quantization noise in discrete wavelet transform filters for image processing
由: Chervyakov, N. I., и др.
出版: (2018)

Method of cleaning hair structures for intellectual image classification of skin neoplasms
由: Lyakhova, U. A., и др.
出版: (2021)

Comparative Analysis of Despeckle Filtering Methods for Ultrasound Images
由: Baboshina, V. A., и др.
出版: (2023)

Implementation of Smoothing Image Filtering in the Residue Number System
由: Chervyakov, N. I., и др.
出版: (2019)

High Performance Parallel Pseudorandom Number Generator on Cellular Automata
由: Lyakhov, P. A., и др.
出版: (2022)

Method for determining skin lesions from images using neural network
由: Lyakhova, U. A., и др.
出版: (2020)

Residue number system-based solution for reducing the hardware cost of a convolutional neural network
由: Chervyakov, N. I., и др.
出版: (2020)

Control of the latent image formed as a combination of variously oriented textures
由: Zharkih, A. A., и др.
出版: (2019)

Area-efficient FPGA implementation of minimalistic convolutional neural network using residue number system
由: Chervyakov, N. I., и др.
出版: (2019)

Hardware implementation of video processing device using residue number system
由: Chervyakov, N. I., и др.
出版: (2019)

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
由: Valueva, M. V., и др.
出版: (2020)

High-speed smoothing filter in the residue number system
由: Chervyakov, N. I., и др.
出版: (2018)

High-performance digital image filtering architectures in the residue number system based on the Winograd method
由: Valueva, M. V., и др.
出版: (2022)

Increasing of convolutional neural network performance using residue number system
由: Chervyakov, N. I., и др.
出版: (2018)

Hardware implementation of a convolutional neural network using calculations in the residue number system
由: Chervyakov, N. I., и др.
出版: (2019)

RNS-based image processing
由: Chervyakov, N. I., и др.
出版: (2018)

RNS-based image processing
由: Chervyakov, N. I., и др.
出版: (2018)

Analysis of the quantization noise of linear time-invariant filters for image processing
由: Lyakhov, P. A., и др.
出版: (2019)

Classification of moduli sets for residue number system with special diagonal functions
由: Chervyakov, N. I., и др.
出版: (2020)

Two methods of adaptive median filtering of impulse noise in images
由: Chervyakov, N. I., и др.
出版: (2018)

How country image affects intention to visit a destination: Evidence from russian tourists visiting switzerland
由: Belozerova, O. I., и др.
出版: (2019)

Optimization of neural network training for image recognition based on trigonometric polynomial approximation
由: Vershkov, N. A., и др.
出版: (2022)

Comparative Analysis of Various Methods to Circuit Design for DWT with CDF 9/7 Wavelet
由: Nagornov, N. N., и др.
出版: (2022)

Single image super-resolution method based on bilinear interpolation and U-Net combination
由: Lyakhov, P. A., и др.
出版: (2021)

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity
由: Lyakhov, P. A., и др.
出版: (2023)

High performance parallel computing in residue number system
由: Deryabin, M. A., и др.
出版: (2018)

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

A new method for adaptive median filtering of images
由: Lyakhov, P. A., и др.
出版: (2019)

User interface design for a web-based image processing and analysis system
由: Rechitskiy, A. S., и др.
出版: (2018)

Image recognition by a second-order convolutional neural network with dynamic receptive fields
由: Nemkov, R. M., и др.
出版: (2018)

System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing
由: Lyakhova, U. A., и др.
出版: (2021)

Efficiency analysis of the image impulse noise cleaning using median filters with weighted central element
由: Chervyakov, N. I., и др.
出版: (2018)