一种基于生成对抗网络与注意力机制的可见光和红外图像融合方法

针对低照度可见光图像中目标难以识别的问题，提出了一种新的基于生成对抗网络的可见光和红外图像的融合方法，该方法可直接用于RGB三通道的可见光图像和单通道红外图像的融合。在生成对抗网络中，生成器采用具有编码层和解码层的U-Net结构，判别器采用马尔科夫判别器，并引入注意力机制模块，使得融合图像可以更关注红外图像上的高强度信息。实验结果表明，该方法在维持可见光图像细节纹理信息的同时，引入红外图像的主要目标信息，生成视觉效果良好、目标辨识度高的融合图像，并在信息熵、结构相似性等多项客观指标上表现良好。

A Visible and Infrared Image Fusion Method based on Generative Adversarial Networks and Attention Mechanism

A new fusion method for visible and infrared images based on generative adversarial networks is proposed to solve the problem of recognizing targets in low-light images; the method can be directly applied to the fusion of RGB three-channel visible images and infrared images. In generative adversarial networks, the generator adopts a U-Net structure with encoding and decoding layers. The discriminator adopts a Markovian discriminator, and the attention mechanism is introduced to force the fused image to pay more attention to the high-intensity information on infrared images. The experimental results show that the proposed method not only maintains the detailed texture information of visible images but also introduces the main target information of infrared images to generate fusion images with good visual effects and high target identification, and it performs well in information entropy, structural similarity, and other objective indexes.