Infrared and visible image fusionMulti-resolution preservationInformation query interactionImplicit neural representationsThe infrared and visible image fusion (IVIF) task aims to generate high-resolution images
2、设计元特征嵌入网络,生成元特征来弥合IVIF网络与OD网络之间的差异,使OD网络提高的高层语义特征能更好地指导IVIF网络融合更多目标语义信息。 创新点 1、首次提出利用元学习思想设计元特征嵌入网络,生成根据IVIF网络能力学习得到的元特征,使元特征天然兼容IVIF网络。 2、提出IVIF网络和OD网络互相促进学习方法,通过IV...
MetaFusion是一种创新的图像处理技术,它通过元特征嵌入(Meta-Feature Embedding, MFE)实现了红外和可见光图像的融合。下面是对这一技术的详细解释: 1. MetaFusion的概念 MetaFusion是一种基于元学习的图像融合方法,它通过引入元特征嵌入网络(MFE)来弥合红外图像融合(IVIF)和目标检测(OD)任务之间的差异。MetaFusion旨...
Infrared and visible image fusion via detail preserving adversarial learning DetailGAN: 通过细节保留对抗性学习实现红外和可见光图像融合 研究背景 现有的方法通常选择源图像的相同显著特征,例如边缘和线条,以集成到融合图像中,使得融合图像包含更多的细节信息。然而,上述方法可能不适用于红外和可见光图像融合。特别地,...
Although deep neural networks efficiently process large-scale object information such as contour, edge, and contrast, they fail to effectively handle the extraction of details, such as textures. In practice, image fusion has been applied to combine information in infrared (IR) and visible (VIS) ...
Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption. A fire-new survey for infrared and visible image fusion. - RollingPlain/IVIF_ZOO
DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion(用于红外和可见光图像融合的深度图像分解)
1(c), the thermal target is significantly weakened, and the fused image of Fig. 1(d) lacks texture information. In this article, we present a unique infrared and visible image fusion model on the basis of infrared background suppression. To mitigate cross-modal interference, we develop a ...
NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial/Channel Attention Models NestFuse: 基于嵌套连接和空间/通道注意力模型的红外可见图像融合体系结构 研究背景 尽管基于SR和LRR的融合方法已经显示出非常好的性能。这些方法仍有以下缺点。a) 融合算法的运行时间高度依赖于...
论文题目:DetFusion: A Detection-driven Infrared and Visible Image Fusion Network 作者:Yiming Sun; Bing Cao; Pengfei Zhu; Qinghua Hu 会议论文集:Proceedings of the 30th ACM International Conference on Multimedia 年份:2022.10 研究目标: 利用两种模式之间的互补信息来合成一个包含更丰富信息的新图像,有助于...