Optimization algorithm of the velocity field determining in image processing

Bazhanov, Pavel; Kotina, E. D.; Ovsyannikov, Dmitri; Ploskikh, Victor

doi:10.35470/2226-4116-2018-7-4-174-181

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…Image super-resolution is a classic problem related to image processing [Bazhanov et al, 2018], and there have been a number of researchers involved in this area [Cheng et al, 2019;Ma et al, 2020]. Yang et al [Yang et al, 2014] provided a general review of previously standard techniques when applying convolutional neural networks to their research.…”

Section: Literature Reviewmentioning

confidence: 99%

Artwork style transfer model using deep learning approach

Hien

Huy

Hieu

2021

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Art in general and fine arts, in particular, play a significant role in human life, entertaining and dispelling stress and motivating their creativeness in specific ways. Many well-known artists have left a rich treasure of paintings for humanity, preserving their exquisite talent and creativity through unique artistic styles. In recent years, a technique called ’style transfer’ allows computers to apply famous artistic styles into the style of a picture or photograph while retaining the shape of the image, creating superior visual experiences. The basic model of that process, named ’Neural Style Transfer,’ has been introduced promisingly by Leon A. Gatys; however, it contains several limitations on output quality and implementation time, making it challenging to apply in practice. Based on that basic model, an image transform network was proposed in this paper to generate higher-quality artwork and higher abilities to perform on a larger image amount. The proposed model significantly shortened the execution time and can be implemented in a real-time application, providing promising results and performance. The outcomes are auspicious and can be used as a referenced model in color grading or semantic image segmentation, and future research focuses on improving its applications.

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Section: Literature Reviewmentioning

confidence: 99%

Artwork style transfer model using deep learning approach

Hien

Huy

Hieu

2021

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“…Different approaches are known from publications and widely used in practice [Barron and Fleet, 1994;Bruhn, Weickert and Schnorr, 2005;Sun, Roth and Black, 2010;Tu, et.al., 2019]. In the current work the problem of constructing the velocity field is considered as control and optimization problem [Ovsyannikov, 1980;Bazhanov, et al, 2018;Kotina and Ovsyannikov, 2021].…”

Section: Introductionmentioning

confidence: 99%

Optimization approach to the velocity field determining problem

Kotina

Ovsyannikov

Elizarova

2022

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A reconstruction of the velocity field is of current interest for different theoretical and practical problems. In the presented work the optimization method of the velocity field construction for successive images is developed and generalized using the concepts of trajectory beam (an ensemble of trajectories), a distribution density function (brightness), and a quality functional. A new formulation of the problem is proposed with the splitting of the initial image to subsets and taking into account a temporal variation. Analytical expressions of the variation and gradient of the investigated functional are given, allowing one to construct various directed optimization methods. The proposed approach can be used for determining the velocity field within different tasks, particularly, for the problems of diagnostic image processing.

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“…A large number of publications are devoted to the data processing. In the articles [Kotina and Pasechnaya, 2014;Kotina et al, 2019;Bazhanov et al, 2018], methods of image processing based on the construction of the velocity field are developed. A number of papers discuss issues of the myocardium segmentation [Xu et al, 2009;Germano et al, 2016], the parametric image construction [Slomka et al, 2017;Gomez et al, 2018;Kuronuma et al, 2021][Germano and, software packages for automated quantification [Germano et al, 2007;Ficaro et al, 2007;Garcia et al, 2007], the prognostic value of gated myocardial perfusion SPECT [Nakajima et al, 2017;Kuronuma et al, 2021;Cabrera-Rodríguez et al, 2013].…”

Section: Introductionmentioning

confidence: 99%

Challenges of gated myocardial perfusion SPECT processing

Ploskikh

Kotina

2021

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The paper provides an in-depth look at gated myocardial perfusion single photon emission computed tomography (SPECT) data processing. Attention paid to several unmentioned subjects of the quantitative analysis of gated myocardial perfusion SPECT data. The article considers several options in the construction process of the ellipsoid coordinate system of the left ventricle (LV). Mathematical representation of polar maps is given. Formulas of the regional parameters calculation are proposed. Issues of phase analysis are explored.

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Optimization algorithm of the velocity field determining in image processing

Cited by 12 publications

References 10 publications

Artwork style transfer model using deep learning approach

Artwork style transfer model using deep learning approach

Optimization approach to the velocity field determining problem

Challenges of gated myocardial perfusion SPECT processing

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