Luo Yongyu, Luo Zhongqiang
School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, Sichuan, China.
Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, Sichuan, China.
PeerJ Comput Sci. 2024 Nov 28;10:e2569. doi: 10.7717/peerj-cs.2569. eCollection 2024.
The purpose of infrared and visible image fusion is to obtain an image that includes both infrared target and visible information. However, among the existing infrared and visible image fusion methods, some of them give priority to the fusion effect, often with complex design, ignoring the influence of attention mechanisms on deep features, resulting in the lack of visible light texture information in the fusion image. To solve these problems, an infrared and visible image fusion method based on dense gradient attention residuals is proposed in this article. Firstly, squeeze-and-excitation networks are integrated into the gradient convolutional dense block, and a new gradient attention residual dense block is designed to enhance the ability of the network to extract important information. In order to retain more original image information, the feature gradient attention module is introduced to enhance the ability of detail information retention. In the fusion layer, an adaptive weighted energy attention network based on an energy fusion strategy is used to further preserve the infrared and visible details. Through the experimental comparison on the TNO dataset, our method has excellent performance on several evaluation indicators. Specifically, in the indexes of average gradient (AG), information entropy (EN), spatial frequency (SF), mutual information (MI) and standard deviation (SD), our method reached 6.90, 7.46, 17.30, 2.62 and 54.99, respectively, which increased by 37.31%, 6.55%, 32.01%, 8.16%, and 10.01% compared with the other five commonly used methods. These results demonstrate the effectiveness and superiority of our method.
红外与可见光图像融合的目的是获得包含红外目标和可见光信息的图像。然而，在现有的红外与可见光图像融合方法中，一些方法优先考虑融合效果，设计往往较为复杂，忽略了注意力机制对深层特征的影响，导致融合图像中缺乏可见光纹理信息。为了解决这些问题，本文提出了一种基于密集梯度注意力残差的红外与可见光图像融合方法。首先，将挤压激励网络集成到梯度卷积密集块中，设计了一种新的梯度注意力残差密集块，以增强网络提取重要信息的能力。为了保留更多的原始图像信息，引入了特征梯度注意力模块来增强细节信息保留能力。在融合层，采用基于能量融合策略的自适应加权能量注意力网络进一步保留红外和可见光细节。通过在TNO数据集上的实验比较，我们的方法在几个评估指标上具有优异的性能。具体而言，在平均梯度（AG）、信息熵（EN）、空间频率（SF）、互信息（MI）和标准差（SD）指标上，我们的方法分别达到了6.90、7.46、17.30、2.62和54.99，与其他五种常用方法相比分别提高了37.31%、6.55%、32.01%、8.16%和10.01%。这些结果证明了我们方法的有效性和优越性。
