References
- Artan GG, Tombul GS. The future trends of EO/IR systems for ISR platforms. InImage Sensing Technologies: Materials, Devices, Systems and Applications IX. SPIE. 2022;12091:76-88.
- Gonzalez-Jorge H, Aldao E, Fontenla-Carrera G, Veiga-López F, Balvís E, Ríos-Otero E. Counter Drone Technology: A Review.
- Dudek A, Stütz P. A Cloud Detection System for UAV Sense and Avoid: Flight Experiments to Analyze the Impact of Varying Environmental Conditions. InAIAA SCITECH 2024 Forum. 2024;1859.
- Munir A, Siddiqui AJ, Anwar S. Investigation of uav detection in images with complex backgrounds and rainy artifacts. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024;221-230.
- Ouyang Y, Shi B, Huang X, Lu L, Jiang Y. Research on infrared point target recognition method based on space-based early warning system. InInternational Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023) SPIE. 2024; 12969:697-701.
- Kim BH, Kim MY, Chae YS. Background registration-based adaptive noise filtering of LWIR/MWIR imaging sensors for UAV applications. Sensors. 2017;18(1):60.
- Shang X, Li G, Jiang Z, Zhang S, Ding N, Liu J. Holistic dynamic frequency transformer for image fusion and exposure correction. Information Fusion. 2024;102:102073.
- Singh N, Bhat A. A robust model for improving the quality of underwater images using enhancement techniques. Multimed Tools Appl. 2024;83(1):2267-88.
- Sharma M. A review: image fusion techniques and applications. Int J Comput Sci Inf Technol. 2016;7(3):1082-5.
- Xiao X, Li C, He H, Huang J, Yu T. Rotating machinery fault diagnosis method based on multi-level fusion framework of multi-sensor information. Information Fusion. 2025;113:102621.
- Li S, Kang X, Fang L, Hu J, Yin H. Pixel-level image fusion: A survey of the state of the art.. Information Fusion. 2017;33:100-12.
- Liu S, Shi M, Zhu Z, Zhao J. Image fusion based on complex-shearlet domain with guided filtering. Multidimensional Systems and Signal Processing. 2017;28(1):207-24.
- Bhutto JA, Lianfang T, Du Q, Soomro TA, Lubin Y, Tahir MF. An enhanced image fusion algorithm by combined histogram equalization and fast gray level grouping using multi-scale decomposition and gray-PCA. IEEE Access. 2020;8:157005-21.
- Lee T, Kim S. Research trend analysis for EO-IR image registration. In: 2022 22nd International Conference on Control, Automation and Systems (ICCAS). IEEE. 2022;1288-91.
- Velesaca HO, Bastidas G, Rouhani M, Sappa AD. Multimodal image registration techniques: a comprehensive survey. Multimedia Tools and Applications. 2024;83(23):63919-47
- Patil MS. Interpolation techniques in image resampling. Int. J. Eng. Technol. 2018;7(3.34):567-70.
- Han D. Comparison of commonly used image interpolation methods. InConference of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Atlantis Press. 2013;1556-1559).
- Parsania PS, Virparia PV. A comparative analysis of image interpolation algorithms. International Journal of Advanced Research in Computer and Communication Engineering. 2016;5(1):29-34.
- Kaur R, Singh S, Sethi GK. Spatial and Spectral Analysis of Resampling Algorithms in Image Fusion of Optical and Microwave Satellite Images: A Case Study Over Western Himalayas. Journal of the Indian Society of Remote Sensing. 2024;52(10):2317-34.
- Papadopoulos S, Koukiou G, Anastassopoulos V. Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification-A Review. Journal of Imaging. 2024;10(1):15.
- Chen J, Chen L, Shabaz M. Image fusion algorithm at pixel level based on edge detection. Journal of Healthcare Engineering. 2021;(1):5760660.
- Zhou M, Huang J, Yan K, Hong D, Jia X, Chanussot J, Li C. A general spatial-frequency learning framework for multimodal image fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2024.
- Mishra VK, Kumar R, Nareti U, Pant T, Soni PK. Pansharpening using IHS method on multi-sensor data and multiple feature extraction using modified Otsu thresholding. Journal of the Indian Society of Remote Sensing. 2024;52(1):113-26.
- Jiang D, Zhuang D, Huang Y, Fu J. Survey of multispectral image fusion techniques in remote sensing applications. Image fusion and its applications. 2011;1:1-22.
- Alhatami E, Huang M, Bhatti UA. Image fusion techniques and applications for remote sensing and medical images. InDeep Learning for Multimedia Processing Applications. CRC Press. 2024;154-175.
- Smith LI. A tutorial on principal components analysis.
- Li S, Kang X, Hu J. Image fusion with guided filtering. IEEE Transactions on Image processing. 2013;22(7):2864-75.
- Zhang X, Wang X, Yan C, Sun Q. EV-fusion: A novel infrared and low-light color visible image fusion network integrating unsupervised visible image enhancement. IEEE Sensors Journal. 2024;24(4):4920-34.
- Dogra A, Goyal B, Agrawal S. From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications. IEEE access. 2017;5:16040-67.
- Gong X, Hou Z, Wan Y, Zhong Y, Zhang M, Lv K. Multispectral and SAR image fusion for multiscale decomposition based on least squares optimization rolling guidance filtering. IEEE Transactions on Geoscience and Remote Sensing. 2024;62:1-20.
- Yin H, Li Y, Chai Y, Liu Z, Zhu Z. A novel sparse-representation-based multi-focus image fusion approach. Neurocomputing. 2016;216:216-29.
- Ma X, Hu S, Liu S, Fang J, Xu S. Remote sensing image fusion based on sparse representation and guided filtering. Electronics. 2019;8(3):303.
- Li L, Lv M, Jia Z, Ma H. Sparse representation-based multi-focus image fusion method via local energy in shearlet domain. Sensors. 2023;23(6):2888.
- Li L, Shi Y, Lv M, Jia Z, Liu M, Zhao X, Zhang X, Ma H. Infrared and visible image fusion via sparse representation and guided filtering in laplacian pyramid domain. Remote Sensing. 2024;16(20):3804.
- Singh P, Bhandari AK. Laplacian and gaussian pyramid based multiscale fusion for nighttime image enhancement. Multimedia Tools and Applications. 2025;84(15):15527-51.
- Xi Y, Liu D, Kou R, Zhang J, Yu W. Gradient Enhanced Feature Pyramid Network for Infrared Small Target Detection. IEEE Geoscience and Remote Sensing Letters. 2025.
- Mehta B, Patel H, Nanavati M, Limbad N, Gohel P. Implementation and comparative analysis of various Pyramid-based Image Fusion techniques for Multimodal MRI images of brain. Journal of Integrated Science and Technology. 2025;13(2):1031.
- Naidu VP. Discrete cosine transform based image fusion techniques. Journal of Communication, Navigation and Signal Processing. 2012;1(1):35-45.
- Singh R, Khare A. Multiscale medical image fusion in wavelet domain. The Scientific World Journal. 2013;1:521034.
- Kekre HB, Athawale A, Sadavarti D. Algorithm to generate Kekre’s Wavelet transform from Kekre’s Transform. International Journal of Engineering Science and Technology. 2010;2(5):756-67.
- Kekre HB, Sarode T, Dhannawat R. Implementation and comparison of different transform techniques using Kekre’s wavelet transform for image fusion. International Journal of Computer Applications. 2012;44(10):41-8.
- Dhannawat R, Sarode T. Kekre’s hybrid wavelet transform technique with dct, walsh, hartley and kekre’s transform for image fusion. International Journal of Computer Engineering and Technology (IJCET). 2013;4(1):195-202.
- Kekre Hb, Sarode T, Dhannawat R. Image fusion using Kekre’s hybrid wavelet transform. In2012 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE; 2012: 1-6.
- Danyal MM, Khan S, Khan RS, Jan S, Rahman N. Enhancing Multi-Modality Medical Imaging: A Novel Approach with Laplacian Filter+Discrete Fourier Transform Pre-Processing and Stationary Wavelet Transform Fusion. J. Intell. Med. Healthc. 2024;2:35-53.
- Udomhunsakul S, Yamsang P, Tumthong S, Borwonwatanadelok P. Multiresolution edge fusion using SWT and SFM. InProceedings of the world congress on engineering 2011;2:6-8.
- Naseem S, Mahmood T, Khan AR, Farooq U, Nawazish S, Alamri FS, Saba T. Image Fusion Using Wavelet Transformation and XGboost Algorithm. Computers, Materials & Continua. 2024;79(1).
- Dong L, Yang Q, Wu H, Xiao H, Xu M. High quality multi-spectral and panchromatic image fusion technologies based on curvelet transform. Neurocomputing. 2015;159:268-74.
- An FP, Ma XM, Bai L. Image fusion algorithm based on unsupervised deep learning-optimized sparse representation. Biomedical Signal Processing and Control. 2022;71:103140.
- Cheng P, Xiong Z, Bao Y, Zhuang P, Zhang Y, Blasch E, Chen G. A deep learning-enhanced multi-modal sensing platform for robust human object detection and tracking in challenging environments. Electronics. 2023;12(16):3423.
- Sreeja G, Saraniya O. Image fusion through deep convolutional neural network. InDeep learning and parallel computing environment for bioengineering systems. Academic Press. 2019; 37-52.
- Liu Y, Chen X, Peng H, Wang Z. Multi-focus image fusion with a deep convolutional neural network. Information Fusion. 2017;36:191-207.
- Du C, Gao S. Image segmentation-based multi-focus image fusion through multi-scale convolutional neural network. IEEE access. 2017;5:15750-61.
- Masi G, Cozzolino D, Verdoliva L, Scarpa G. Pansharpening by convolutional neural networks. Remote Sensing. 2016 Jul 14;8(7):594.
- Liu Y, Chen X, Ward RK, Wang ZJ. Image fusion with convolutional sparse representation. IEEE signal processing letters. 2016;23(12):1882-6.
- Huang W, Xiao L, Wei Z, Liu H, Tang S. A new pan-sharpening method with deep neural networks. IEEE Geoscience and Remote Sensing Letters. 2015;12(5):1037-41.
- Xu H, Zhang H, Ma J. Classification saliency-based rule for visible and infrared image fusion. IEEE Transactions on Computational Imaging. 2021;7:824-36.
- Singh S, Singh H, Bueno G, Deniz O, Singh S, Monga H, Hrisheekesha PN, Pedraza A. A review of image fusion: Methods, applications and performance metrics. Digital Signal Processing. 2023;137:104020.
- Zeng Y, Wang X, Zhao H, Jin Y, Giannopoulos GA, Li Y. Image fusion methods in high-speed railway scenes: A survey. High-speed Railway. 2023;1(2):87-91.
- Zhu P, Ouyang W, Guo Y, Zhou X. A Two-To-One Deep Learning General Framework for Image Fusion. Frontiers in bioengineering and biotechnology. 2022;10:923364.
- Zhao Z, Xu S, Zhang C, Liu J, Zhang J. Bayesian fusion for infrared and visible images. Signal Processing. 2020;177:107734.
- Zhang C, Hu H, Tai Y, Yun L, Zhang J. Trustworthy image fusion with deep learning for wireless applications. Wireless Communications and Mobile Computing. 2021;1:6220166.
- Xu S, Zhao Z, Wang Y, Zhang C, Liu J, Zhang J. Deep convolutional sparse coding networks for image fusion. arXiv preprint arXiv:2005.08448. 2020.
- Ma J, Xu H, Jiang J, Mei X, Zhang XP. DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Transactions on Image Processing. 2020;29:4980-95.
- Li H, Cen Y, Liu Y, Chen X, Yu Z. Different input resolutions and arbitrary output resolution: A meta learning-based deep framework for infrared and visible image fusion. IEEE Transactions on Image Processing. 2021;30:4070-83.