Volume: 2 Issue: 1
Year: 2025, Page: 29-31, Doi: https://doi.org/10.70968/ijeaca.v2i1.D1019
Received: March 20, 2025 Accepted: May 10, 2025 Published: June 18, 2025
Multimodal image fusion is a rapidly evolving domain that combines complementary information from different imaging modalities into a unified representation, enhancing visual perception and decision-making in areas such as medical diagnostics, surveillance, and remote sensing. This paper surveys the taxonomy of multimodal fusion tasks, discusses classical and emerging deep learning-based methods including convolutional neural networks, autoencoders, generative adversarial networks, and transformer-based architectures and outlines key evaluation metrics. Through this comprehensive review, we highlight recent trends, performance benchmarks, and future directions for effective multimodal image fusion.
Keywords: Emerging Advances in Multimodal Imaging and Fusion Techniques
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© 2025 Vyavahare & Sawant. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Vyavahare AJ, Sawant DA. (2025). Emerging Advances in Multimodal Imaging and Fusion Techniques. International Journal of Electronics and Computer Applications. 2(1): 29-31. https://doi.org/10.70968/ijeaca.v2i1.D1019