High-performance digital image filtering architectures in the residue number system based on the Winograd method

Valueva, M.V.; Lyakhov, P.A.; Nagornov, N.N.; Valuev, G.V.

doi:10.18287/2412-6179-co-933

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…, имеют широкое практическое применение, что подтверждается их использованием при проведении исследований в работах [9,11,12,18]. Системы обработки изображений используют множество различных фильтров для обработки изображений разного размера.…”

Section: алгоритмы построения матриц преобразования для метода виногр...unclassified

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity

Lyakhov,

Nagornov,

Semyonova

et al. 2023

Computer Optics

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The fast increase of the amount of quantitative and qualitative characteristics of digital visual data calls for the improvement of the performance of modern image processing devices. This article proposes new algorithms for 2D digital image processing based on the Winograd method in a general form. An analysis of the obtained results showed that the use of the Winograd method reduces the computational complexity of image processing by up to 84% compared to the traditional direct digital filtering method depending on the filter parameters and image fragments, while not affecting the quality of image processing. The resulting Winograd method transformation matrices and the algorithms developed can be used in image processing systems to improve the performance of the modern microelectronic devices that carry out image denoising, compression, and pattern recognition. Research directions that show promise for further research include hardware implementation on a field-programmable gate array and application-specific integrated circuit, development of algorithms for digital image processing based on the Winograd method in a general form for a 1D wavelet filter bank and for stride convolution used in convolutional neural networks.

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Section: алгоритмы построения матриц преобразования для метода виногр...unclassified

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity

Lyakhov,

Nagornov,

Semyonova

et al. 2023

Computer Optics

View full text Add to dashboard Cite

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Automatic medical specialty classification based on patients’ description of their symptoms

Mao

Chen

2023

BMC Med Inform Decis Mak

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In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register the correct specialty for the first time if they do not receive help from the hospitals. In this study, we try to automatically direct the patients to the appropriate specialty based on the symptoms they described. As far as we know, this is the first study to solve the problem. We propose a neural network-based model based on a hybrid model integrated with an attention mechanism. To prove the actual effect of this hybrid model, we utilized a data set of more than 40,000 items, including eight departments, such as Otorhinolaryngology, Pediatrics, and other common departments. The experiment results show that the hybrid model achieves more than 93.5% accuracy and has a high generalization capacity, which is superior to traditional classification models.

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Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms

Rybakova,

Limonova,

Nikolaev

2024

Applied Sciences

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Gaussian filtering, being a convolution with a Gaussian kernel, is a widespread technique in image analysis and computer vision applications. It is the traditional approach for noise reduction. In some cases, performing the exact convolution can be computationally expensive and time-consuming. To address this problem, approximations of the convolution are often used to achieve a balance between accuracy and computational efficiency, such as with running sums, Bell blur, Deriche approximation, etc. At the same time, modern computing devices support data parallelism (vectorization) via Single Instruction Multiple Data (SIMD) and can process integer numbers faster than floating-point approaches. In this paper, we describe several methods for approximating a Gaussian filter, implement the SIMD and quantized versions, and compare them in terms of speed and accuracy. The experiments were performed on central processing units with a x86_64 architecture using a family of SSE SIMD extensions and an ARMv8 architecture using the NEON SIMD extension. All the optimized approximations demonstrated 10–20× speedup while maintaining the accuracy in the range of 1 × 10−5 or higher. The fastest method is a trivial Stack blur with a relatively high error, so we recommend using the second-order Vliet–Young–Verbeek filter and quantized Bell blur and running sums as more accurate and still computationally efficient alternatives.

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High-performance digital image filtering architectures in the residue number system based on the Winograd method

Cited by 3 publications

References 25 publications

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity

Automatic medical specialty classification based on patients’ description of their symptoms

Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms

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