Structural Networks for Image Application

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 236

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: view/image separation; image generation

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Guest Editor
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: object detection; computer vision; medical image analysis and processing; pattern recognition

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Guest Editor
1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
2. Guangdong Province Key Laboratory of Information Security Technology, Guangzhou, China
3. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Beijing, China
Interests: computer vision; 3D human video prediction and generation; multimodal video understanding; multimodal large models; ocean large models; trajectory prediction
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Guest Editor
College of Artificial Intelligence, Nanjing University of Aeronauticsand Astronautics, Nanjing 210023, China
Interests: artificial intelligence security; autonomous driving security; IoT security; computational visual security; software vulnerability analysis; formal methods; embedded systems; edge AI; microservices

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Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Interests: deep learning; artificial intelligence; 3D vision; image super-resolution; video enhancement

Special Issue Information

Dear Colleagues,

Structural networks are a transformative approach to image application, integrating principles from graph theory, topology, computational geometry, deep learning, and more. They focus on modeling complex relationships within image data. This paradigm enables the capture of hierarchical, spatial, and semantic dependencies critical for advanced image understanding. As an important component of neural networks, structural networks enable models to parse intricate visual scenes, infer contextual relationships, and generate robust representations that transcend pixel-level analysis.

The scope of this Special Issue encompasses advances in structural network methodologies and their innovative applications in imaging domains, including, but not limited to, computer vision, pattern recognition, natural language understanding, intelligent robotics, and deep learning.

This Special Issue aims to build a collaborative community for researchers to present cutting-edge developments, propose novel frameworks, and bridge theoretical innovations with real-world implementations. We invite papers on topics including, but not limited to, architectural design, biomedical diagnostics, autonomous navigation, satellite imagery analysis, augmented reality and efficient learning for large-scale/high-resolution images. We welcome theoretically rigorous and experimentally validated contributions that advance the frontier of structural networks in imaging. Papers may span algorithms, architectures, benchmarks, and interdisciplinary case studies.

Dr. Chunwei Tian
Dr. Shuai Wu
Dr. Jian-Fang Hu
Dr. Yinbo Yu
Dr. Xin Wang
Guest Editors

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Keywords

  • deep learning,
  • deep networks
  • image processing
  • optimization methods
  • statistical methods

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Published Papers (1 paper)

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Research

19 pages, 7161 KiB  
Article
Dynamic Snake Convolution Neural Network for Enhanced Image Super-Resolution
by Weiqiang Xin, Ziang Wu, Qi Zhu, Tingting Bi, Bing Li and Chunwei Tian
Mathematics 2025, 13(15), 2457; https://doi.org/10.3390/math13152457 - 30 Jul 2025
Abstract
Image super-resolution (SR) is essential for enhancing image quality in critical applications, such as medical imaging and satellite remote sensing. However, existing methods were often limited in their ability to effectively process and integrate multi-scales information from fine textures to global structures. To [...] Read more.
Image super-resolution (SR) is essential for enhancing image quality in critical applications, such as medical imaging and satellite remote sensing. However, existing methods were often limited in their ability to effectively process and integrate multi-scales information from fine textures to global structures. To address these limitations, this paper proposes DSCNN, a dynamic snake convolution neural network for enhanced image super-resolution. DSCNN optimizes feature extraction and network architecture to enhance both performance and efficiency: To improve feature extraction, the core innovation is a feature extraction and enhancement module with dynamic snake convolution that dynamically adjusts the convolution kernel’s shape and position to better fit the image’s geometric structures, significantly improving feature extraction. To optimize the network’s structure, DSCNN employs an enhanced residual network framework. This framework utilizes parallel convolutional layers and a global feature fusion mechanism to further strengthen feature extraction capability and gradient flow efficiency. Additionally, the network incorporates a SwishReLU-based activation function and a multi-scale convolutional concatenation structure. This multi-scale design effectively captures both local details and global image structure, enhancing SR reconstruction. In summary, the proposed DSCNN outperforms existing methods in both objective metrics and visual perception (e.g., our method achieved optimal PSNR and SSIM results on the Set5 ×4 dataset). Full article
(This article belongs to the Special Issue Structural Networks for Image Application)
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