Application of Artificial Intelligence in Visual Processing

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 3676

Special Issue Editor

Director, Very Large Scale Integration (VLSI) Research Center, Macquarie University, Sydney 2109, Australia
Interests: artificial intelligence; deep learning; visual processing; video technology; biomedical image processing

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on “Application of Artificial Intelligence in Visual Processing”.

Scientific articles reporting results achieved and proposals for new ways of looking at AI problems are welcome. Both of them must include demonstrations of value and effectiveness. The broad aspects of AI constitute advances in the overall field of visual processing, including but not limited to receiving, relaying, and processing visual information.

Papers describing applications of AI are also welcome, but the focus should be on how new and novel AI methods advance performance in application areas, rather than presenting yet another application of conventional AI methods. 

Prof. Dr. Yinan Kong
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
  • deep learning
  • visual processing
  • video technology
  • biomedical image processing

Published Papers (3 papers)

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Research

13 pages, 4413 KiB  
Article
WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
by Yi Li, Nan Wang, Jinlong Li and Yu Zhang
Appl. Sci. 2023, 13(22), 12513; https://doi.org/10.3390/app132212513 - 20 Nov 2023
Viewed by 721
Abstract
Although the existing deblurring methods for defocused images are capable of approximately recovering clear images, they still exhibit certain limitations, such as ringing artifacts and remaining blur. Along these lines, in this work, a novel deep-learning-based method for image defocus deblurring was proposed, [...] Read more.
Although the existing deblurring methods for defocused images are capable of approximately recovering clear images, they still exhibit certain limitations, such as ringing artifacts and remaining blur. Along these lines, in this work, a novel deep-learning-based method for image defocus deblurring was proposed, which can be applied to medical images, traffic monitoring, and other fields. The developed approach is equipped with wavelet transform, an iterative filter adaptive module, and graph neural network and was specifically designed for handling defocused blur. Our network exhibits excellent properties in preserving the original information during the restoration of clear images, thereby enhancing its capability to spatially address varying blurriness and improving the quality of deblurring. From the acquired experimental results, the superiority of the introduced method in the context of image defocus deblurring compared to the majority of the existing algorithms was clearly demonstrated. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Processing)
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25 pages, 12580 KiB  
Article
Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision
by Mingju Chen, Zhengxu Duan, Zhongxiao Lan and Sihang Yi
Appl. Sci. 2023, 13(11), 6407; https://doi.org/10.3390/app13116407 - 24 May 2023
Cited by 6 | Viewed by 1234
Abstract
At present, Chinese 3D reconstruction solutions using stereo cameras mainly face known, indoor, structured scenes; for the reconstruction of unstructured, larger-scale scenes with a large variety of texture information of different intensities, there are certain difficulties in ensuring accuracy and real-time processing. For [...] Read more.
At present, Chinese 3D reconstruction solutions using stereo cameras mainly face known, indoor, structured scenes; for the reconstruction of unstructured, larger-scale scenes with a large variety of texture information of different intensities, there are certain difficulties in ensuring accuracy and real-time processing. For the above problems, we propose a scene reconstruction method using stereo vision. Firstly, considering the influence of outdoor lighting and weather on the captured 2D images, the optimized SAD-FAST feature detection algorithm and stereo-matching strategy were employed in the stereo-matching stage to improve the overall efficiency and matching quality at this stage. Then, a homogenized feature extraction algorithm with gradient value decreasing step by step (GVDS) was used in the depth value calculation to ensure a sufficient number of feature points for strong texture information while extracting features from weak-texture areas, which greatly improved the quality and speed of unstructured scene reconstruction. We conducted experiments to validate the proposed method, and the results showed the feasibility of the proposed method and its high practical value. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Processing)
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14 pages, 1109 KiB  
Article
Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
by Xizhan Gao, Kang Wei, Jia Li, Ziyu Shi, Hui Zhao and Sijie Niu
Appl. Sci. 2023, 13(11), 6383; https://doi.org/10.3390/app13116383 - 23 May 2023
Viewed by 823
Abstract
As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computational speed. Combining [...] Read more.
As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computational speed. Combining these two types of methods to obtain fast and accurate recognition results remains a challenging problem. Motivated by this, in this study, a novel Manifolds-based Low-Rank Dictionary Pair Learning (MbLRDPL) method was developed for a set-based video recognition/image set classification task. Specifically, each video or image set was first modeled as a covariance matrix or linear subspace, which can be seen as a point on a Riemannian manifold. Second, the proposed MbLRDPL learned discriminative class-specific synthesis and analysis dictionaries by clearly imposing the nuclear norm on the synthesis dictionaries. The experimental results show that our method achieved the best classification accuracy (100%, 72.16%, 95%) on three datasets with the fastest computing time, reducing the errors of state-of-the-art methods (JMLC, DML, CEBSR) by 0.96–75.69%. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Processing)
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