A review of image fusion: Methods, applications and performance metrics

Singh, Simrandeep; Singh, Harbinder; Bueno, Gloria; Déniz, Óscar; Singh, Sartajvir; Monga, Himanshu; Hrisheekesha, P.N.; Pedraza, Aníbal

doi:10.1016/j.dsp.2023.104020

Cited by 12 publications

(1 citation statement)

References 223 publications

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“…Therefore, image fusion techniques are emerging, in which infrared and visible image fusion is the most widely used. Infrared and visible image fusion (IVIF) produces fused images with complementary characteristics and richer information than either modality alone [2][3][4]. The resulting images are visually appealing and, more importantly, beneficial for practical applications such as medical diagnostics [5], remote sensing [6], automotive assistance [7], video surveillance [8], and wildfire monitoring [9].…”

Section: Introductionmentioning

confidence: 99%

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion

Yang,

Xiang,

Chen

et al. 2024

Sensors

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The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This paper proposes a fast and efficient recursive fusion neural network model to solve this complex problem that few people have touched. Specifically, we designed an attention module combining a traditional fusion knowledge prior with channel attention to extract modal-specific features efficiently. We used a shared attention layer to perform the early fusion of modal-shared features. Adopting parallel dilated convolution layers further reduces the network’s parameter count. Our network is trained recursively, featuring minimal model parameters, and requires only a few training batches to achieve excellent fusion results. This significantly reduces the consumption of time, space, and computational resources during model training. We compared our method with nine SOTA methods on three public datasets, demonstrating our method’s efficient training feature and good fusion results.

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