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Article

A Comprehensive Image Quality Evaluation of Image Fusion Techniques Using X-Ray Images for Detonator Detection Tasks

1
Laboratory of Signals and Systems (LSS), Faculty of Science and Technology, Abdelhamid Ibn Badis University of Mostaganem, Route Nationale N° 11 Kharouba, Mostaganem 27000, Algeria
2
Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunărea de Jos University of Galați, 800008 Galați, Romania
3
Modelling & Simulation Laboratory (MSlab), Dunărea de Jos, University of Galați, 800008 Galați, Romania
4
Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos, University of Galați, 800008 Galați, Romania
5
Department of Physics, School of Science and Technology, Sefako Makgatho Health Sciences University, Medunsa, Pretoria 0204, South Africa
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(20), 10987; https://doi.org/10.3390/app152010987 (registering DOI)
Submission received: 3 September 2025 / Revised: 3 October 2025 / Accepted: 8 October 2025 / Published: 13 October 2025

Abstract

Purpose: Luggage X-rays suffer from low contrast, material overlap, and noise; dual-energy imaging reduces ambiguity but creates colour biases that impair segmentation. This study aimed to (1) employ connotative fusion by embedding realistic detonator patches into real X-rays to simulate threats and enhance unattended detection without requiring ground-truth labels; (2) thoroughly evaluate fusion techniques in terms of balancing image quality, information content, contrast, and the preservation of meaningful features. Methods: A total of 1000 X-ray luggage images and 150 detonator images were used for fusion experiments based on deep learning, transform-based, and feature-driven methods. The proposed approach does not need ground truth supervision. Deep learning fusion techniques, including VGG, FusionNet, and AttentionFuse, enable the dynamic selection and combination of features from multiple input images. The transform-based fusion methods convert input images into different domains using mathematical transforms to enhance fine structures. The Nonsubsampled Contourlet Transform (NSCT), Curvelet Transform, and Laplacian Pyramid (LP) are employed. Feature-driven image fusion methods combine meaningful representations for easier interpretation. Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Random Forest (RF), and Local Binary Pattern (LBP) are used to capture and compare texture details across source images. Entropy (EN), Standard Deviation (SD), and Average Gradient (AG) assess factors such as spatial resolution, contrast preservation, and information retention and are used to evaluate the performance of the analysed methods. Results: The results highlight the strengths and limitations of the evaluated techniques, demonstrating their effectiveness in producing sharpened fused X-ray images with clearly emphasized targets and enhanced structural details. Conclusions: The Laplacian Pyramid fusion method emerges as the most versatile choice for applications demanding a balanced trade-off. This is evidenced by its overall multi-criteria balance, supported by a composite (geometric mean) score on normalised metrics. It consistently achieves high performance across all evaluated metrics, making it reliable for detecting concealed threats under diverse imaging conditions.
Keywords: X-ray images of luggage; detonator images; deep learning image fusion; transform-based image fusion; feature-level image fusion X-ray images of luggage; detonator images; deep learning image fusion; transform-based image fusion; feature-level image fusion

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MDPI and ACS Style

Oulhissane, L.; Merah, M.; Moldovanu, S.; Moraru, L. A Comprehensive Image Quality Evaluation of Image Fusion Techniques Using X-Ray Images for Detonator Detection Tasks. Appl. Sci. 2025, 15, 10987. https://doi.org/10.3390/app152010987

AMA Style

Oulhissane L, Merah M, Moldovanu S, Moraru L. A Comprehensive Image Quality Evaluation of Image Fusion Techniques Using X-Ray Images for Detonator Detection Tasks. Applied Sciences. 2025; 15(20):10987. https://doi.org/10.3390/app152010987

Chicago/Turabian Style

Oulhissane, Lynda, Mostefa Merah, Simona Moldovanu, and Luminita Moraru. 2025. "A Comprehensive Image Quality Evaluation of Image Fusion Techniques Using X-Ray Images for Detonator Detection Tasks" Applied Sciences 15, no. 20: 10987. https://doi.org/10.3390/app152010987

APA Style

Oulhissane, L., Merah, M., Moldovanu, S., & Moraru, L. (2025). A Comprehensive Image Quality Evaluation of Image Fusion Techniques Using X-Ray Images for Detonator Detection Tasks. Applied Sciences, 15(20), 10987. https://doi.org/10.3390/app152010987

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