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Applied Sciences
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AI-Driven Image and Signal Processing
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AI-Driven Image and Signal 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: 31 July 2026 | Viewed by 2445

Special Issue Editors

Prof. Dr. Yi Zhao

E-Mail Website
Guest Editor
School of Science, Harbin Institute of Technology, Shenzhen 518055, China
Interests: information; high dimensional data; topological data analysis; urban dynamics; q-analysis; integration entropy
Special Issues, Collections and Topics in MDPI journals
Dr. Wang Yifeng

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Interests: deep generative models; signal processing and analysis; representation learning; human–computer interaction; physical AI

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is transforming the landscape of image and signal processing, offering powerful tools for handling complex data, automating workflows, and uncovering meaningful patterns. This Special Issue aims to explore the latest advancements and applications of AI, including machine learning and deep learning, in a wide range of image and signal processing domains.

We invite high-quality contributions that address theoretical developments, practical implementations, and interdisciplinary applications. Topics of interest include, but are not limited to, the following:

  • AI-based techniques for image enhancement, restoration, and reconstruction;
  • Machine learning models for signal classification, denoising, and compression;
  • Deep learning approaches used in medical imaging and biomedical signal analysis;
  • Computer vision applications in robotics, autonomous systems, and surveillance;
  • Real-time AI algorithms for audio, speech, and multimedia processing;
  • Novel frameworks for multimodal data fusion and interpretation.

This Special Issue will provide a platform for researchers, engineers, and practitioners to present cutting-edge research that will further advance the use of AI in image and signal processing. By showcasing state-of-the-art methodologies and real-world applications, we aim to foster collaboration amongst scholars, as well as inspire new directions for the design of intelligent systems that are capable of addressing challenges across diverse scientific and industrial domains.

Prof. Dr. Yi Zhao
Dr. Wang Yifeng
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 250 words) can be sent to the Editorial Office for assessment.

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 (AI)
  • image and signal processing
  • deep learning
  • machine learning
  • computer vision

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

23 pages, 5498 KB  
Article
Unsupervised Magnetic Anomaly Detection Method Based on Granular Ball One-Class Classification
by Yuwei Pan, Haigang Ren, Xu Li, Jianwei Li and Boxin Zuo
Appl. Sci. 2026, 16(9), 4472; https://doi.org/10.3390/app16094472 - 2 May 2026
Viewed by 189
Abstract
In complex marine environments, underwater magnetic anomaly detection is challenging because target magnetic anomaly signals are typically weak and easily overwhelmed by background magnetic noise. Although deep learning-based methods have significantly improved detection capability, most existing approaches still rely on abundant labeled target [...] Read more.
In complex marine environments, underwater magnetic anomaly detection is challenging because target magnetic anomaly signals are typically weak and easily overwhelmed by background magnetic noise. Although deep learning-based methods have significantly improved detection capability, most existing approaches still rely on abundant labeled target data, which is difficult to obtain in practical applications. To address this challenge, this paper proposes an unsupervised underwater magnetic anomaly detection method based on Gaussian granular ball one-class classification (GBOC). A density-guided hierarchical partitioning strategy is introduced to divide the latent space into multiple compact high-density regions and construct corresponding Gaussian granular ball representations. This strategy enables more effective modeling of complex background magnetic noise and improves anomaly detection under low signal-to-noise ratio (SNR) conditions. Experimental results show that the proposed method achieves robust performance across different SNR levels in the unsupervised setting. Compared with other methods, it yields a higher detection rate and more stable results under a fixed false alarm rate. Furthermore, a semi-supervised magnetic anomaly detection method is developed by introducing a small amount of prior information on magnetic anomalies. Experimental results demonstrate that the proposed semi-supervised method can further improve detection accuracy while maintaining good robustness and stability. Full article
(This article belongs to the Special Issue AI-Driven Image and Signal Processing)
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20 pages, 48606 KB  
Article
GMUD-Net: Global Modulated Unbalanced Dual-Branch Network for Image Restoration in Various Degraded Environments
by Shengchun Wang, Yingjie Liu and Huijie Zhu
Appl. Sci. 2026, 16(6), 2854; https://doi.org/10.3390/app16062854 - 16 Mar 2026
Viewed by 310
Abstract
Image restoration has wide applications in the field of computer vision, yet existing methods suffer from limitations. CNNs struggle to capture long-range dependencies, while transformers exhibit insufficient performance in handling local details and high computational complexity. Additionally, existing dual-branch networks fail to define [...] Read more.
Image restoration has wide applications in the field of computer vision, yet existing methods suffer from limitations. CNNs struggle to capture long-range dependencies, while transformers exhibit insufficient performance in handling local details and high computational complexity. Additionally, existing dual-branch networks fail to define a clear dominant–auxiliary role between branches, leading to redundancy and high computational costs. This paper proposes a Global Modulated Unbalanced Dual-Branch Network (GMUD-Net), which innovatively adopts an unbalanced structure with a CNN as the main branch and a transformer as the auxiliary branch. Specifically, the CNN branch achieves strong restoration capability by integrating the global–local hybrid backbone block (GLBB) and the frequency-based global attention module (FGAM). As the key building block in the CNN branch, GLBB integrates a local backbone branch, a global Fourier branch, and a residual branch to fuse local details with global context. Meanwhile, FGAM leverages the fast Fourier transform at the bottleneck to enhance cross-channel interaction and improve global restoration performance. In addition, the lightweight transformer branch employs efficient cross-channel attention to provide complementary global cues, which are filtered and injected into the CNN branch via the global attention guidance block (GAG). These designs integrate the advantages of both CNNs and transformers while significantly reducing computational burden, offering a new paradigm to address the limitations of traditional dual-branch architectures. Experimental results demonstrate that compared with existing algorithms, the proposed method achieves state-of-the-art or highly competitive performance in both quantitative evaluations and qualitative results across nine datasets. Full article
(This article belongs to the Special Issue AI-Driven Image and Signal Processing)
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24 pages, 4667 KB  
Article
A Unified Complementary Regularization Framework for Long-Tailed Image Classification
by Xingyu Shen, Lei Zhang, Lituan Wang and Yan Wang
Appl. Sci. 2026, 16(3), 1656; https://doi.org/10.3390/app16031656 - 6 Feb 2026
Viewed by 579
Abstract
Class imbalance is a formidable and ongoing challenge in image classification tasks. Existing methods address this issue by emphasizing minority classes through class redistribution in the feature space or adjusting decision boundaries. Although such approaches improve the accuracy of minority classes, they often [...] Read more.
Class imbalance is a formidable and ongoing challenge in image classification tasks. Existing methods address this issue by emphasizing minority classes through class redistribution in the feature space or adjusting decision boundaries. Although such approaches improve the accuracy of minority classes, they often lead to unstable training and performance degradation on majority classes. To alleviate these challenges, we propose a unified redistribution framework termed as ComReg, which explicitly enforces complementary regularization on feature learning and decision boundary optimization in long-tailed image classification. Specifically, ComReg employs a multi-expert learning framework combined with prior-knowledge-guided online distillation to construct distribution-aware decision boundaries. From the feature space learning perspective, we enhance intra-class compactness and inter-class separability through decoupled-balanced contrastive learning. To further align the distributions in both spaces, we introduce a delay-weighted prototype learning strategy, which incorporates the decision boundary constructed by the head-class expert into the decoupled-balanced contrastive learning process. Extensive experiments on widely used long-tailed benchmarks, including CIFAR10-LT and CIFAR100-LT, as well as the real-world long-tailed datasets such as subsets of MedMNIST v2, demonstrate that our method achieves state-of-the-art performance. Full article
(This article belongs to the Special Issue AI-Driven Image and Signal Processing)
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45 pages, 4286 KB  
Article
CrossPhire: Benefiting Multimodality for Robust Phishing Web Page Identification
by Ahmad Hani Abdalla Almakhamreh and Ahmet Selman Bozkir
Appl. Sci. 2026, 16(2), 751; https://doi.org/10.3390/app16020751 - 11 Jan 2026
Cited by 1 | Viewed by 908
Abstract
Phishing attacks continue to evolve and exploit fundamental human impulses, such as trust and the need for a rapid response, as well as emotional triggers. This makes the human mind both a valuable asset and a significant vulnerability. The proliferation of zero-day vulnerabilities [...] Read more.
Phishing attacks continue to evolve and exploit fundamental human impulses, such as trust and the need for a rapid response, as well as emotional triggers. This makes the human mind both a valuable asset and a significant vulnerability. The proliferation of zero-day vulnerabilities has been identified as a significant exacerbating factor in this threat landscape. To address these evolving challenges, we introduce CrossPhire: a multimodal deep learning framework with an end-to-end architecture that captures semantic and visual cues from multiple data modalities, while also providing methodological insights for anti-phishing multimodal learning. First, we demonstrate that markup-free semantic text encoding captures linguistic deception patterns more effectively than DOM-based approaches, achieving 96–97% accuracy using textual content alone and providing the strongest single-modality signal through sentence transformers applied to HTML text stripped of structural markup. Second, through controlled comparison of fusion strategies, we show that simple concatenation outperforms a sophisticated gating mechanism so-called Mixture-of-Experts by 0.5–10% when modalities provide complementary, non-redundant security evidence. We validate these insights through rigorous experimentation on five datasets, achieving competitive same-dataset performance (97.96–100%) while demonstrating promising cross-dataset generalization (85–96% accuracy under distribution shift). Additionally, we contribute Phish360, a rigorously curated multimodal benchmark with 10,748 samples addressing quality issues in existing datasets (96.63% unique phishing HTML vs. 16–61% in prior benchmarks), and provide LIME-based explainability tools that decompose predictions into modality-specific contributions. The rapid inference time (0.08 s) and high accuracy results position CrossPhire as a promising solution in the fight against phishing attacks. Full article
(This article belongs to the Special Issue AI-Driven Image and Signal Processing)
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