Infrared Physics & Technology, Journal Year: 2024, Volume and Issue: unknown, P. 105671 - 105671
Published: Dec. 1, 2024
Language: Английский
Infrared Physics & Technology, Journal Year: 2024, Volume and Issue: unknown, P. 105671 - 105671
Published: Dec. 1, 2024
Language: Английский
Journal of Visual Communication and Image Representation, Journal Year: 2024, Volume and Issue: 101, P. 104179 - 104179
Published: May 1, 2024
Infrared and visible image fusion represents a significant segment within the domain. The recent surge in processing hardware advancements, including GPUs, TPUs, cloud computing platforms, has facilitated of extensive datasets from multiple sensors. Given remarkable proficiency neural networks feature extraction fusion, their application infrared emerged as prominent research area years. This article begins by providing an overview current mainstream algorithms for based on networks, detailing principles various algorithms, representative works, respective advantages disadvantages. Subsequently, it introduces domain-relevant datasets, evaluation metrics, some typical scenarios. Finally, conducts qualitative quantitative evaluations results state-of-the-art offers future prospects experimental results.
Language: Английский
Citations
12Optics and Lasers in Engineering, Journal Year: 2023, Volume and Issue: 173, P. 107925 - 107925
Published: Nov. 9, 2023
Language: Английский
Citations
20Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 176, P. 108094 - 108094
Published: Feb. 8, 2024
Language: Английский
Citations
7Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 181, P. 108435 - 108435
Published: July 19, 2024
Language: Английский
Citations
6Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 176, P. 108042 - 108042
Published: Jan. 29, 2024
Language: Английский
Citations
5Signal Processing, Journal Year: 2024, Volume and Issue: 225, P. 109620 - 109620
Published: July 24, 2024
Language: Английский
Citations
3Optics and Lasers in Engineering, Journal Year: 2025, Volume and Issue: 186, P. 108800 - 108800
Published: Jan. 24, 2025
Language: Английский
Citations
0Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 717 - 717
Published: Jan. 24, 2025
This paper presents a novel image fusion method designed to enhance the integration of infrared and visible images through use residual attention mechanism. The primary objective is generate fused that effectively combines thermal radiation information from with detailed texture background images. To achieve this, we propose multi-level feature extraction framework encodes both shallow deep features. In this framework, features are utilized as queries, while function keys values within cross-attention module. architecture enables more refined process by selectively attending integrating relevant different levels. Additionally, introduce dynamic preservation loss optimize process, ensuring retention critical details source Experimental results demonstrate proposed outperforms existing techniques across various quantitative metrics delivers superior visual quality.
Language: Английский
Citations
0Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(6)
Published: May 3, 2025
Language: Английский
Citations
0Infrared Physics & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 105906 - 105906
Published: May 1, 2025
Language: Английский
Citations
0