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Advances and Application of Video and Digital Image Processing &...
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Advances and Application of Video and Digital Image Processing & Deep Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 4139

Special Issue Editor

Dr. Pavel Lyakhov

E-Mail Website
Guest Editor
Department of Applied Mathematics and Mathematical Modeling, North-Caucasus Federal University, 355009 Stavropol, Russia
Interests: high performance computing; residue number system arithmetic; digital signal processing; digital image processing; machine learning; artificial intelligence; medical imaging; custom hardware development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in object recognition using deep learning technologies have made a huge contribution to the development of artificial intelligence systems. The problem of detecting objects in images and videos is currently present in almost all areas of human activity. Computer vision systems can already recognize characters on paper and documents, signatures, objects within images, and people's faces. Such technologies greatly ease the work of humans and minimize errors caused by the human factor. Digital video processing makes a significant contribution to security—video surveillance programs in buildings and on the streets, cameras fixing the speed of a car in a traffic flow, searching for people by face, as well as in augmented reality applications. The task of detecting objects in video sequences requires processing functionality and flexible analysis. The benefits of using computer vision systems are extensive: flexibility, speed, continuity, and the ability to process more information in less time, thus freeing up human resources. Scientists around the world are working to improve, complement, and develop new ways to use deep learning. It is becoming increasingly popular due to the fast learning abilities of neural networks, their generalized and highly specialized nature, the use of parallel computing, and the combination of information technology and biomedicine. The systems that currently exist have already been implemented in many areas of life—medicine, security services, manufacturing, forecasting, robotics, the creation of expert systems, and others.

The latest technological developments in video and digital image processing and deep learning will be shared through this Special Issue. We invite researchers and investigators to contribute their original research or review articles to this Special Issue.

Prof. Dr. Pavel Lyakhov
Guest Editor

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. Applied Sciences 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 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

  • artificial intelligence
  • machine learning
  • deep learning
  • digital image processing
  • digital video processing
  • pattern recognition
  • convolutional neural networks
  • parallel computing

Published Papers (3 papers)

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Research

19 pages, 4806 KiB  
Article
Video-Restoration-Net: Deep Generative Model with Non-Local Network for Inpainting and Super-Resolution Tasks
by Yuanfeng Zheng, Yuchen Yan and Hao Jiang
Appl. Sci. 2023, 13(18), 10001; https://doi.org/10.3390/app131810001 - 05 Sep 2023
Viewed by 712
Abstract
Although deep learning-based approaches for video processing have been extensively investigated, the lack of generality in network construction makes it challenging for practical applications, particularly in video restoration. As a result, this paper presents a universal video restoration model that can simultaneously tackle [...] Read more.
Although deep learning-based approaches for video processing have been extensively investigated, the lack of generality in network construction makes it challenging for practical applications, particularly in video restoration. As a result, this paper presents a universal video restoration model that can simultaneously tackle video inpainting and super-resolution tasks. The network, called Video-Restoration-Net (VRN), consists of four components: (1) an encoder to extract features from each frame, (2) a non-local network that recombines features from adjacent frames or different locations of a given frame, (3) a decoder to restore the coarse video from the output of a non-local block, and (4) a refinement network to refine the coarse video on the frame level. The framework is trained in a three-step pipeline to improve training stability for both tasks. Specifically, we first suggest an automated technique to generate full video datasets for super-resolution reconstruction and another complete-incomplete video dataset for inpainting, respectively. A VRN is then trained to inpaint the incomplete videos. Meanwhile, the full video datasets are adopted to train another VRN frame-wisely and validate it against authoritative datasets. We show quantitative comparisons with several baseline models, achieving 40.5042 dB/0.99473 on PSNR/SSIM in the inpainting task, while during the SR task we obtained 28.41 dB/0.7953 and 27.25/0.8152 on BSD100 and Urban100, respectively. The qualitative comparisons demonstrate that our proposed model is able to complete masked regions and implement super-resolution reconstruction in videos of high quality. Furthermore, the above results show that our method has greater versatility both in video inpainting and super-resolution tasks compared to recent models. Full article
(This article belongs to the Special Issue Advances and Application of Video and Digital Image Processing & Deep Learning)
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16 pages, 4208 KiB  
Article
Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
by Anzor Orazaev, Pavel Lyakhov, Valentina Baboshina and Diana Kalita
Appl. Sci. 2023, 13(3), 1585; https://doi.org/10.3390/app13031585 - 26 Jan 2023
Cited by 3 | Viewed by 1707
Abstract
Images taken with different sensors and transmitted through different channels can be noisy. In such conditions, the image most often suffers from random-valued impulse noise. Denoising an image is an important part of image preprocessing before recognition by a neural network. The accuracy [...] Read more.
Images taken with different sensors and transmitted through different channels can be noisy. In such conditions, the image most often suffers from random-valued impulse noise. Denoising an image is an important part of image preprocessing before recognition by a neural network. The accuracy of image recognition by a neural network directly depends on the intensity of image noise. This paper presents a three-stage image cleaning and recognition system, which includes a developed detector of pulsed noisy pixels, a filter for cleaning found noisy pixels based on an adaptive median, and a neural network program for recognizing cleaned images. It was noted that at low noise intensities, cleaning is practically not required, but noise with an intensity of more than 10% can seriously damage the image and reduce recognition accuracy. As a training base for noise, cleaning, and recognition, the CIFAR10 digital image database was used, consisting of 60,000 images belonging to 10 classes. The results show that the proposed neural network recognition system for images affected by to random-valued impulse noise effectively finds and corrects damaged pixels. This helped to increase the accuracy of image recognition compared to existing methods for cleaning random-valued impulse noise. Full article
(This article belongs to the Special Issue Advances and Application of Video and Digital Image Processing & Deep Learning)
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15 pages, 9651 KiB  
Article
Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images
by Zhipeng Ren, Jianping Zhao, Chunyi Chen, Yan Lou and Xiaocong Ma
Appl. Sci. 2023, 13(3), 1245; https://doi.org/10.3390/app13031245 - 17 Jan 2023
Cited by 1 | Viewed by 1041
Abstract
Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality [...] Read more.
Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality of the acquired image may not be ideal for follow-up research to make suitable judgment. In order to obtain clearer images, we propose a dual-path adversarial generation network model algorithm that particularly improves the accuracy of the satellite remote sensing image super-resolution. This network involves a dual-path convolution operation in a generator structure, a feature mapping attention mechanism that first extracts important feature information from a low-resolution image, and an enhanced deep convolutional network to extract the deep feature information of the image. The deep feature information and the important feature information are then fused in the reconstruction layer. Furthermore, we also improve the algorithm structure of the loss function and discriminator to achieve a relatively optimal balance between the output image and the discriminator, so as to restore the super-resolution image closer to human perception. Our algorithm was validated on the public UCAS-AOD datasets, and the obtained results showed significantly improved performance compared to other methods, thus exhibiting a real advantage in supporting various image-related field applications such as navigation monitoring. Full article
(This article belongs to the Special Issue Advances and Application of Video and Digital Image Processing & Deep Learning)
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