Effect of RNS dynamic range on grayscale images filtering

Chervyakov, Nikolay I.; Lyakhov, Pavel; Kalita, Diana I.; Shulzhenko, K.S.

doi:10.1109/red.2016.7779323

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…In addition, the choice of enough RNS dynamic range is a very important question too. In [40], authors considered the influence of the RNS dynamic range on the quality of image filtering. Therefore, it is necessary to choose optimal moduli sets, so we propose the method of RNS construction with a convenient form of DF.…”

Section: Construction Methods Of Rns With Hardware Efficient Dfmentioning

confidence: 99%

Construction of Residue Number System Using Hardware Efficient Diagonal Function

et al. 2019

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The residue number system (RNS) is a non-positional number system that allows one to perform addition and multiplication operations fast and in parallel. However, because the RNS is a non-positional number system, magnitude comparison of numbers in RNS form is impossible, so a division operation and an operation of reverse conversion into a positional form containing magnitude comparison operations are impossible too. Therefore, RNS has disadvantages in that some operations in RNS, such as reverse conversion into positional form, magnitude comparison, and division of numbers are problematic. One of the approaches to solve this problem is using the diagonal function (DF). In this paper, we propose a method of RNS construction with a convenient form of DF, which leads to the calculations modulo 2 n , 2 n − 1 or 2 n + 1 and allows us to design efficient hardware implementations. We constructed a hardware simulation of magnitude comparison and reverse conversion into a positional form using RNS with different moduli sets constructed by our proposed method, and used different approaches to perform magnitude comparison and reverse conversion: DF, Chinese remainder theorem (CRT) and CRT with fractional values (CRTf). Hardware modeling was performed on Xilinx Artix 7 xc7a200tfbg484-2 in Vivado 2016.3 and the strategy of synthesis was highly area optimized. The hardware simulation of magnitude comparison shows that, for three moduli, the proposed method allows us to reduce hardware resources by 5.98-49.72% in comparison with known methods. For the four moduli, the proposed method reduces delay by 4.92-21.95% and hardware costs by twice as much by comparison to known methods. A comparison of simulation results from the proposed moduli sets and balanced moduli sets shows that the use of these proposed moduli sets allows up to twice the reduction in circuit delay, although, in several cases, it requires more hardware resources than balanced moduli sets. such as field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC). All these attractive features increase interest to RNS in the areas where large amounts of computation are needed. The applications of RNS are digital signal processing [2][3][4], cryptography [5][6][7], digital image processing [8], cloud computing [9], Internet of Things [10] and others. In [11], the authors propose a technique to estimate real-valued numbers by means of the Chinese remainder theorem (CRT), employing for this goal a Kroenecker based M-Estimation, to improve robustness. A new method based on the Chinese remainder theorem (CRT) is proposed for absolute position computation in [12]. This has advantages in terms of hardware and flexibility because it does not use memory. The authors of [13] offer to use RNS to improve the performance of the convolutional neural network developed for pattern recognition tasks. Reference [14] describes the method of construction for finite impulse response filers using RNS.However, the limitations of RNS include some operations such a...

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Section: Construction Methods Of Rns With Hardware Efficient Dfmentioning

confidence: 99%

Construction of Residue Number System Using Hardware Efficient Diagonal Function

et al. 2019

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“…Various SFs and bit-widths r corresponding to the known and proposed implementation methods presented in Table 1 were used. Both the RNS dynamic range requirements [76] were calculated, and the moduli sets were selected for each method of 8-bit image processing from Table 1 (Table 3). Hardware implementation results of 3D medical image WP using different filter coefficients bit-width r and RNS parallel computing and their comparison with BNS from are presented in Table 4.…”

Section: B Hardware Implementations Of Medical Image Wp With Sfc and ...mentioning

confidence: 99%

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

et al. 2022

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Medical imaging using different modalities has many problems. The main ones are low informativeness, various distortion noises, and a large amount of information. Fusion, denoising, and visual data compression are used to solve them in practice. Discrete wavelet transform is one way to implement various fusion, denoising, and compression methods for 2D and 3D medical image processing. Medical imaging systems produce increasingly accurate images with scanning technology and digital devices development. These images have improved quality using both higher spatial resolutions and color bit-depth. Processing a large volume of medical imaging data requires considerable resources and processing time. Modern wavelet-based devices for medical image processing do not meet the current performance demand. Hardware accelerators are being designed to solve this problem. This paper proposes new (fieldprogrammable gate array) FPGA accelerators using wavelet processing (WP) with scaled filter coefficients (SFC) and parallel computing in residue number system (RNS) to improve the performance of high-quality 3D medical image WP systems. The computational complexity is reduced using the developed WP method with SFC and the proposed wavelet filter coefficients scaling algorithm. Parallel computing is organized in RNS using moduli sets of a particular type. Hardware implementation of 3D medical image WP using the proposed FPGA accelerators increases device performance by 2.89-3.59 times, increasing the hardware resources by 1.18-3.29 times compared to state-of-the-art solutions. The device performance improvement is achieved while maintaining high-quality 3D medical image processing in peak signal-to-noise ratio terms.INDEX TERMS Medical image processing, discrete wavelet transform, scaled filter coefficients, residue number system, high-performance computing, hardware accelerator, field-programmable gate arrays.

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“…In [1], the choice of a set of RNS modules occurs after reading the pixel values of the images, which can lead to incorrect results of the system operation and image processing [9,10].…”

Section: Architectural Organization Of the Image Filtering Processmentioning

confidence: 99%

“…These advantages, consisting in the development of a sufficiently high system speed and low power consumption, emphasize the advantages of using RNS in the applications of the digital image processing [8]. In [9,10,12,13], the actual issue that arises in the digital image processing is described, namely the choice of the correct set of modules.…”

Section: Numerical Implementation Of Filtering By Example Gray Scale ...mentioning

confidence: 99%

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The Numerical Implementation of the Image Filtering Method with Computations in the Residue Number System

Alsaedi

2021

J. Al-Qadisiyah Comp. Sci. Math.

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The use of the Residue Number System (RNS) instead of the known binary arithmetic proposes, it is substantiated that the application of the non-position RNS will allow increasing the speed and reducing power consumption of digital image processing applications. Analyzed known schemes, based on which the process of image filtration occurs. The inefficiency of the application of known schemes is substantiated, connected with the possibility of errors due to the overflow of the dynamic range of the RNS. A new scheme of the filtration transformation process is given, which allows obtaining correct results of digital processing, independent of the size of the digital image being processed and the filter mask. The obtained results can be used in the construction of specialized digital image processing systems with low power consumption and high speed of operation.

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Effect of RNS dynamic range on grayscale images filtering

Cited by 5 publications

References 10 publications

Construction of Residue Number System Using Hardware Efficient Diagonal Function

Construction of Residue Number System Using Hardware Efficient Diagonal Function

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

The Numerical Implementation of the Image Filtering Method with Computations in the Residue Number System

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