Special Issues
Table of Content

Development and Application of Deep Learning and Image Processing

Submission Deadline: 30 April 2026 View: 559 Submit to Special Issue

Guest Editors

Assoc. Prof. Ching-Lung Fan

Email: p93228001@ntu.edu.tw

Affiliation: Department of Civil Engineering, The Republic of China Military Academy, Kaohsiung 830, Taiwan

Homepage:

Research Interests: Computer Vision; Image Processing Technology; Deep Learning; Machine Learning; Rremote Sensing


Prof. Kuei-Hu Chang

Email: evenken2002@yahoo.com.tw

Affiliation: Department of Management Sciences, R.O.C. Military Academy, Fengshan, 83059, Taiwan

Homepage:

Research Interests: Fuzzy logic; Linguistic Algorithms; Natural Language Processing; Military Domain Knowledge Discovering; Soft Computing; Risk Assessment; Supply Chain; Reliability


Summary

Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have revolutionized Image Processing and Computer Vision, enabling unprecedented capabilities in Semantic Segmentation, Imagery Analysis, and Multi-Modal Data Fusion. These technologies are driving innovations in autonomous systems, medical diagnostics, remote sensing, and industrial automation. However, challenges such as model efficiency, interpretability, and generalization across diverse data modalities remain critical research frontiers. This Special Issue seeks to explore cutting-edge methodologies and applications that push the boundaries of AI-driven visual understanding.

Topics include but are not limited to the following:
· Deep learning for few-shot medical image segmentation
· Real-time anomaly detection in manufacturing using lightweight vision transformers
· Explainable AI for structural defect localization in engineering systems


Keywords

Artificial Intelligence, Deep Learning, Image Processing, Computer Vision, Semantic Segmentation, Imagery Analysis, Multi-Modal Data Fusion

Published Papers


  • Open Access

    ARTICLE

    High-Resolution UAV Image Classification of Land Use and Land Cover Based on CNN Architecture Optimization

    Ching-Lung Fan
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077260
    (This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)
    Abstract Unmanned aerial vehicle (UAV) images have high spatial resolution and are cost-effective to acquire. UAV platforms are easy to control, and the prevalence of UAVs has led to an emerging field of remote sensing technologies. However, the details of high-resolution images often lead to fragmented classification results and significant scale differences between objects. Additionally, distinguishing between objects on the basis of shape or textural characteristics can be difficult. Conventional classification methods based on pixels and objects can indeed be ineffective at detecting complex and fine-scale land use and land cover (LULC) features. Therefore, in this More >

  • Open Access

    ARTICLE

    A Semantic-Guided State-Space Learning Framework for Low-Light Image Enhancement

    Xi Cai, Xiaoqiang Wang, Huiying Zhao, Guang Han
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075756
    (This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)
    Abstract Low-light image enhancement (LLIE) remains challenging due to underexposure, color distortion, and amplified noise introduced during illumination correction. Existing deep learning–based methods typically apply uniform enhancement across the entire image, which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction. To overcome these limitations, we propose a Semantic-Guided Visual Mamba Network (SGVMNet) that unifies semantic reasoning, state-space modeling, and mixture-of-experts routing for adaptive illumination correction. SGVMNet comprises three key components: (1) a semantic modulation module (SMM) that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant (LLaVA) and… More >

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