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Symmetry
Special Issues
Multidimensional Signal Processing and Deep Learning—Symmetry Approach
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Special Issue "Multidimensional Signal Processing and Deep Learning—Symmetry Approach"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 15 December 2023 | Viewed by 521

Special Issue Editors

Prof. Dr. Roumen Kountchev
*
E-Mail Website
Guest Editor
Faculty of Telecommunications, Department of Radio Communications and Video Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: 3D image representation; image compression; medical image enhancement; pattern recognition; 3D signal processing; image watermarking; deep learning
* This Guest Editor has passed away.
Special Issues, Collections and Topics in MDPI journals
Dr. Rumen Mironov

E-Mail
Guest Editor
Faculty of Telecommunications, Department of Radio Communications and Video Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: image processing; multidimensional signal processing; pattern recognition; programming; digital signage systems
Special Issues, Collections and Topics in MDPI journals
Dr. Roumiana Kountcheva

E-Mail Website
Guest Editor
TK Engineering, 1712 Sofia, Bulgaria
Interests: image processing; image compression and watermarking; CNCs; programmable controllers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multidimensional (MD) signal processing covers various signals (video, audio, X-ray, multispectral, multi-view, and many others), transferred via contemporary communication systems and networks, and analyzed in medical institutions, traffic control systems, forensic investigations, etc. The objective of this SI is to collect and present contemporary research and achievements in the area of the MD signal processing, aimed at multidisciplinary fields of study: analysis and recognition of MD images, compression, and super-resolution; efficient transfer of MD images; MD computer vision; learning of deep neural networks for MD image processing; generic and fuzzy segmentation of objects in MD images, extraction of MD images from databases; intelligent processing of multispectral and multi-view images; web-based search of MD images; forensic and medical analysis; MD image interpolation; visualization; virtual and augmented reality, based on the concept for processing MD signals, using the symmetrical properties of their contents and structure. All these analyses and research topics will be the basis for various applications in the related scientific areas.

Prof. Dr. Roumen Kountchev
Dr. Rumen Mironov
Dr. Roumiana Kountcheva
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetry
  • multidimensional signal processing
  • deep learning
  • deep neural tensor network
  • tensor image decomposition
  • medical information systems
  • telecommunications
  • 3D computer vision
  • bioinformatics
  • remote ecological monitoring

Published Papers (1 paper)

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Article
Analysis of the Recursive Locally-Adaptive Filtration of 3D Tensor Images
by
Roumen Kountchev
,
Rumen Mironov
and
Roumiana Kountcheva
Symmetry 2023, 15(8), 1493; https://doi.org/10.3390/sym15081493 - 27 Jul 2023
Viewed by 259
Abstract
This work is focused on the computational complexity (CC) reduction of the locally-adaptive processing of 3D tensor images, based on recursive approaches. As a basis, a local averaging operation is used, implemented as a sliding mean 3D filter (SM3DF) with a central symmetric [...] Read more.
This work is focused on the computational complexity (CC) reduction of the locally-adaptive processing of 3D tensor images, based on recursive approaches. As a basis, a local averaging operation is used, implemented as a sliding mean 3D filter (SM3DF) with a central symmetric 3D kernel. Symmetry plays a very important role in constructing the working window. The presented theoretical approach could be adopted in various algorithms for locally-adaptive processing, such as additive noise reduction, sharpness enhancement, texture segmentation, etc. The basic characteristics of the recursive SM3DF are analyzed, together with the main features of the adaptive algorithms for filtration of Gaussian noises and unsharp masking where the filter is aimed at. In the paper, the ability of SM3DF implementation through recursive sliding mean 1D filters, sequentially bonded together, is also introduced. The computational complexity of the algorithms is evaluated for the recursive and non-recursive mode. The recursive SM3DF also suits the 3D convolutional neural networks which comprise sliding locally-adaptive 3D filtration in their layers. As a result of the lower CC, a promising opportunity is opened for higher efficiency of the 3D image processing through tensor neural networks. Full article
(This article belongs to the Special Issue Multidimensional Signal Processing and Deep Learning—Symmetry Approach)
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