Prompt learning and multi-scale attention for infrared and visible image fusion DOI
Yanan Li, Q. Ji, S. Jiao

et al.

Infrared Physics & Technology, Journal Year: 2024, Volume and Issue: unknown, P. 105671 - 105671

Published: Dec. 1, 2024

Language: Английский

A review on infrared and visible image fusion algorithms based on neural networks DOI Creative Commons
Kaixuan Yang, Wei Xiang, Zhenshuai Chen

et al.

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

12
BTSFusion: Fusion of infrared and visible image via a mechanism of balancing texture and salience DOI
Yao Qian, Gang Liu, Haojie Tang

et al.

Optics and Lasers in Engineering, Journal Year: 2023, Volume and Issue: 173, P. 107925 - 107925

Published: Nov. 9, 2023

Language: Английский

Citations

20
MPCFusion: Multi-scale parallel cross fusion for infrared and visible images via convolution and vision Transformer DOI
Haojie Tang, Yao Qian,

Mengliang Xing

et al.

Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 176, P. 108094 - 108094

Published: Feb. 8, 2024

Language: Английский

Citations

7
LiMFusion: Infrared and visible image fusion via local information measurement DOI
Yao Qian, Haojie Tang, Gang Liu

et al.

Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 181, P. 108435 - 108435

Published: July 19, 2024

Language: Английский

Citations

6
MDAN: Multilevel dual-branch attention network for infrared and visible image fusion DOI
Jiawei Wang, Min Jiang, Jun Kong

et al.

Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 176, P. 108042 - 108042

Published: Jan. 29, 2024

Language: Английский

Citations

5
Pedestrian detection-driven cascade network for infrared and visible image fusion DOI
Bowen Zheng,

Hongtao Huo,

Xiaowen Liu

et al.

Signal Processing, Journal Year: 2024, Volume and Issue: 225, P. 109620 - 109620

Published: July 24, 2024

Language: Английский

Citations

3
A new method for fusing infrared and visible images in low-light environments based on visual perception and attention mechanism DOI

Zhen Pei,

Jinbo Lu, Qian Yu

et al.

Optics and Lasers in Engineering, Journal Year: 2025, Volume and Issue: 186, P. 108800 - 108800

Published: Jan. 24, 2025

Language: Английский

Citations

0
Residual Attention-Based Image Fusion Method with Multi-Level Feature Encoding DOI Creative Commons
Hao Li, Tiantian Yang, Runxiang Wang

et al.

Sensors, 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

0
Infrared and visible image fusion network based on low-light image enhancement and attention mechanism DOI
Jinbo Lu,

Zhen Pei,

Jinling Chen

et al.

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(6)

Published: May 3, 2025

Language: Английский

Citations

0
TransFusion: Transfer learning-driven adaptive fusion network for infrared and visible image DOI
Yao Qian,

Rongsheng An,

Gang Liu

et al.

Infrared Physics & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 105906 - 105906

Published: May 1, 2025

Language: Английский

Citations

0