论文标题
Nestfuse：基于巢连接和空间/频道注意模型的红外且可见的图像融合体系结构
NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models
论文作者
论文摘要
在本文中，我们提出了一种新型的红外和可见图像融合方法，在该方法中，我们开发了基于巢连接的网络和空间/通道注意模型。基于NEST连接的网络可以以多尺度的角度从输入数据中保留大量信息。该方法包括三个关键要素：编码器，融合策略和解码器。在我们提出的融合策略中，开发了空间注意模型和通道注意模型，这些模型描述了每个空间位置和每个通道的重要性。首先，将源图像馈入编码器，以提取多尺度的深度特征。然后，开发了新颖的融合策略来融合每个量表的这些特征。最后，融合图像由基于巢连接的解码器重建。实验是在公开可用的数据集上执行的。这些展示我们提出的方法比其他最先进的方法具有更好的融合性能。通过主观和客观评估，这一主张是合理的。我们的融合方法的代码可在https://github.com/hli1221/imagefusion-nestfuse上获得
In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. The approach comprises three key elements: encoder, fusion strategy and decoder respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. Firstly, the source images are fed into the encoder to extract multi-scale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available datasets. These exhibit that our proposed approach has better fusion performance than other state-of-the-art methods. This claim is justified through both subjective and objective evaluation. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse
