Sensors, Vol. 24, Pages 2466: FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion
Sensors, Vol. 24, Pages 2466: FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion
Sensors doi: 10.3390/s24082466
Authors:
Kaixuan Yang
Wei Xiang
Zhenshuai Chen
Yunpeng Liu
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.
Source: Sensors - Category: Biotechnology Authors: Kaixuan Yang Wei Xiang Zhenshuai Chen Yunpeng Liu Tags: Article Source Type: research