%0 Journal Article
%T 一种基于多层卷积稀疏网络的红外与可见光图像融合方法<br>An Infrared and Visible Image Fusion Method Based on Multilayer Convolutional Sparse Network
%A 王静静
%A 王少坤
%A 吕梦莎
%J Computer Science and Application
%P 2562-2574
%@ 2161-881X
%D 2023
%I Hans Publishing
%R 10.12677/CSA.2023.1312255
%X <span style=\"font-size:14px;\">红外图像和可见光图像融合广泛应用于夜视、监视、军事等领域。融合任务的重点在于将可见光和红外光图像中的互补信息整合起来并消除多余信息。此外，大多数融合任务是在低光环境下进行的，如何保持融合结果的照明信息值得研究。为了解决存在的问题，首先，我们设计了一个多级特征模块来融合多源信息。与传统网络的并行层融合策略不同，我们提出了一种并行层和深度层相结合的融合策略。其次，我们在特征提取网络中增加了注意力计算，以提高特征提取网络的性能。第三，为了使融合图像具有良好的照明信息，我们设计了区域照明保留模块，提高了低光环境下融合算法的性能。大量实验证明了所提出的方法具有出色的性能，并且在低光环境下表现更好。此外，所提出的算法在多模式物体检测方面也显示出巨大潜力。</span><br />
<span style=\"font-size:14px;\">Infrared image and visible light image fusion is widely used in night vision, surveillance, military and other fields. The focus of the fusion task is to integrate complementary information in visible and infrared light images and eliminate redundant information. In addition, most of the fusion tasks are performed in the harsh environment of low light, and it is worth studying how to maintain the lighting information of the fusion results. In order to solve the problems existing, firstly, we de-sign a multi-level feature module to fusion multi-source information. Different from the parallel layer fusion strategy of the traditional network, we proposed a fusion strategy that combined par-allel layers and depth layers. Secondly, we add attention computing to the feature extraction net-work to improve the performance of the feature extraction network. Thirdly, in order to make the fusion image have good illumination information, we design the area illumination retention module, improving the performance of the fusion algorithm in low-light environments. A large number of experiments show that the proposed method has excellent performance and will perform better in low-light environments. In addition, the proposed algorithm also shows great potential in multi modal-object detection.</span>
%K 图像融合，红外与可见光，卷积网络<br>Image Fusion
%K Infrared and Visible Light
%K Convolutional Networks
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=78594