New Challenges in Image Processing and Pattern Recognition

Topic Information

Dear Colleagues,

The topic “New Challenges in Image Processing and Pattern Recognition” aims to provide a comprehensive and forward-looking platform for the exchange of innovative research addressing emerging issues, novel methodologies, and multidisciplinary applications in the fields of image processing and pattern recognition. As digital imagery becomes increasingly central across domains—ranging from autonomous systems and biomedical imaging to security, remote sensing, and industrial automation—new challenges continue to arise due to growing data complexity, real-time demands, and the integration of artificial intelligence.

This topic seeks contributions that push the boundaries of current techniques or offer novel perspectives on traditional problems. We welcome both theoretical developments and practical applications that demonstrate clear innovation and impact.

The scope of the topic includes, but is not limited to:

• Advanced image enhancement, restoration, and reconstruction techniques;
• Deep learning architectures and transformer models for image understanding;
• Explainable AI (XAI) and trustworthy pattern recognition systems;
• Real-time and embedded vision systems for edge computing;
• Multimodal data fusion (e.g., RGB-D, thermal, hyperspectral);
• Three-dimensional vision, shape analysis, and object recognition in complex scenes;
• Bio-inspired and physics-informed image analysis models;
• Adversarial attacks, robustness, and secure pattern recognition;
• Image processing in biomedical, environmental, industrial, and artistic domains;
• Benchmarking and evaluation metrics for image processing algorithms.

The topic encourages interdisciplinary approaches and collaborative studies that link computer vision with neuroscience, cognitive science, computational imaging, and other related fields.

Dr. Pei-Ju Chiang
Dr. Cheng-Lun Chen
Dr. Ping-Huan Kuo
Prof. Dr. Wen-Yang Chang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Computers computers	4.2	7.5	2012	16.3 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Journal of Imaging jimaging	3.3	6.7	2015	15.3 Days	CHF 1800	Submit
Machine Learning and Knowledge Extraction make	6.0	9.9	2019	25.5 Days	CHF 1800	Submit
Modelling modelling	1.5	2.2	2020	19.5 Days	CHF 1200	Submit

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Published Papers (1 paper)

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All Applied Sciences Computers Electronics J. Imaging MAKE Modelling

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17 pages, 3889 KB  

Open AccessArticle

STGAN: A Fusion of Infrared and Visible Images

by Liuhui Gong, Yueping Han and Ruihong Li

Electronics 2025, 14(21), 4219; https://doi.org/10.3390/electronics14214219 - 29 Oct 2025

Abstract

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative [...] Read more.

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative Adversarial Network framework based on a Swin Transformer for high-quality infrared and visible image fusion. Firstly, the generator employs a Swin Transformer as its backbone for feature extraction, which adopts a U-Net architecture, and the improved W-MSA is introduced into the bottleneck layer to enhance local attention and improve the expression ability of cross-modal features. Secondly, the discriminator uses a Markov discriminator to distinguish the difference. Then, the core GAN framework is leveraged to guarantee the retention of both infrared thermal radiation and visible-light texture details in the generated image so as to improve the clarity and contrast of the fused image. Finally, simulation verification showed that six out of seven indicators ranked in the top two, especially in key indicators such as PSNR, VIF, MI, and EN, which achieved optimal or suboptimal values. The experimental results on the general dataset show that this method is superior to the advanced method in terms of subjective vision and objective indicators, and it can effectively enhance the fine structure and thermal anomaly information in the image, which gives it great potential in the application of industrial surface defect detection. Full article

(This article belongs to the Topic New Challenges in Image Processing and Pattern Recognition)

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