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Electronics
Special Issues
Signal, Image and Video Processing: Development and Applications
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Special Issue "Signal, Image and Video Processing: Development and Applications"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 October 2023 | Viewed by 1600

Special Issue Editors

Prof. Dr. Xianqiang Yang

E-Mail Website
Guest Editor
Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150001, China
Interests: machine vision; deep learning; artificial intelligence; data-based industrial modeling and measurement
Prof. Dr. Changxing Ding
E-Mail Website
Guest Editor
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
Interests: computer vision; pattern recognition; machine learning
Dr. Zhihao Zhang
E-Mail Website
Guest Editor
College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211800, China
Interests: image processing; machine vision; deep learning; artificial intelligence

Special Issue Information

Dear Colleagues,

Modern complex systems are required to have humanoid intelligence and ability, and vision is an indispensable technical means for complex systems to achieve automation and intelligence. Vision carries rich information regarding a system and its operating environment in the form of signals, images and videos, and through information mining, valuable information or knowledge can be obtained to support the implementation of the automation and intelligence of complex systems. Due to the complexity of the system, the variability and harshness of the operating environment, and the diversity of perception tasks, visual perception faces many challenges in data processing, model development, knowledge expression, etc. This prompts academic researchers and engineering practitioners to make unremitting efforts to promote the development of visual perception technology and its application in various fields.

This Special Issue aims to alleviate the contradiction between the increasing actual demand for visual perception and the backward visual perception technology and focuses on, but is not limited to, advanced machine-learning- and deep-learning-related signal, image, and video-processing technologies. The related topics are data acquisition, data quality enhancement, segmentation, representation and description, feature matching, motion tracking, etc., and technologies related to large-scale/lightweight deep neural networks, domain adaption, and transfer learning with industrial applications are preferred. This Special Issue provides a platform for researchers and practitioners to present original and innovative results regarding new models and methods and engineering solutions.

Prof. Dr. Xianqiang Yang
Prof. Dr. Changxing Ding
Dr. Zhihao Zhang
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. Electronics is an international peer-reviewed open access semimonthly 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 2200 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

  • machine-learning- and deep-learning-based signal, image, and video processing
  • signal, image, and video acquisition
  • data transform and filtering
  • data quality enhancement
  • image and video segmentation
  • image and video representation and description
  • feature extraction, compression, description, and matching based on image and video
  • detection, recognition, classification, and measurement based on image and video
  • motion analysis and object tracking based on image and video
  • large-scale deep neural network development and applications
  • lightweight deep neural network development and applications in embedded devices
  • domain adaptation and transfer learning
  • image- and video-processing-based industrial applications

Published Papers (2 papers)

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Article
An Unsupervised Fundus Image Enhancement Method with Multi-Scale Transformer and Unreferenced Loss
by
Yanzhe Hu
,
Yu Li
,
Hua Zou
and
Xuedong Zhang
Electronics 2023, 12(13), 2941; https://doi.org/10.3390/electronics12132941 - 04 Jul 2023
Viewed by 427
Abstract
Color fundus images are now widely used in computer-aided analysis systems for ophthalmic diseases. However, fundus imaging can be affected by human, environmental, and equipment factors, which may result in low-quality images. Such quality fundus images will interfere with computer-aided diagnosis. Existing methods [...] Read more.
Color fundus images are now widely used in computer-aided analysis systems for ophthalmic diseases. However, fundus imaging can be affected by human, environmental, and equipment factors, which may result in low-quality images. Such quality fundus images will interfere with computer-aided diagnosis. Existing methods for enhancing low-quality fundus images focus more on the overall visualization of the image rather than capturing pathological and structural features at the finer scales of the fundus image sufficiently. In this paper, we design an unsupervised method that integrates a multi-scale feature fusion transformer and an unreferenced loss function. Due to the loss of microscale features caused by unpaired training, we construct the Global Feature Extraction Module (GFEM), a combination of convolution blocks and residual Swin Transformer modules, to achieve the extraction of feature information at different levels while reducing computational costs. To improve the blurring of image details caused by deep unsupervised networks, we define unreferenced loss functions that improve the model’s ability to suppress edge sharpness degradation. In addition, uneven light distribution can also affect image quality, so we use an a priori luminance-based attention mechanism to improve low-quality image illumination unevenness. On the public dataset, we achieve an improvement of 0.88 dB in PSNR and 0.024 in SSIM compared to the state-of-the-art methods. Experiment results show that our method outperforms other deep learning methods in terms of vascular continuity and preservation of fine pathological features. Such a framework may have potential medical applications. Full article
(This article belongs to the Special Issue Signal, Image and Video Processing: Development and Applications)
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Article
Hardware Architecture for Realtime HEVC Intra Prediction
by
Duc Khai Lam
,
Pham The Anh Nguyen
and
Tuan Anh Tran
Electronics 2023, 12(7), 1705; https://doi.org/10.3390/electronics12071705 - 04 Apr 2023
Viewed by 909
Abstract
Researchers have, in recent times, achieved excellent compression efficiency by implementing a more complicated compression algorithm due to the rapid development of video compression. As a result, the next model of video compression, High-Efficiency Video Coding (HEVC), provides high-quality video output while requiring [...] Read more.
Researchers have, in recent times, achieved excellent compression efficiency by implementing a more complicated compression algorithm due to the rapid development of video compression. As a result, the next model of video compression, High-Efficiency Video Coding (HEVC), provides high-quality video output while requiring less bandwidth. However, implementing the intra-prediction technique in HEVC requires significant processing complexity. This research provides a completely pipelined hardware architecture solution capable of real-time compression to minimize computing complexity. All prediction unit sizes of 4×4, 8×8, 16×16, and 32×32, and all planar, angular, and DC modes are supported by the proposed solution. The synthesis results mapped to Xilinx Virtex 7 reveal that our solution can do real-time output with 210 frames per second (FPS) at 1920×1080 resolution, called Full High Definition (FHD), or 52 FPS at 3840×2160 resolution, called 4K, while operating at 232 Mhz maximum frequency. Full article
(This article belongs to the Special Issue Signal, Image and Video Processing: Development and Applications)
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