Research on Infrared and Visible Image Fusion Based on Improved Generative Adversarial Network
收藏ieee-dataport.org2025-03-25 收录
下载链接:
https://ieee-dataport.org/documents/research-infrared-and-visible-image-fusion-based-improved-generative-adversarial-network
下载链接
链接失效反馈官方服务:
资源简介:
The file contains merely a small portion of our research results. The particular picture presented showcases the outcomes that were achieved through our fusion method when applied to the TNO test set. The "ir", which is the abbreviation for infrared light image, is characterized by being rich in thermal radiation information. However, it unfortunately has a rather low spatial resolution. On the other hand, the "vis", representing the visible light image, is abundant in scene information. But it has a drawback in that the human targets within it are not so distinctly visible. The "our" indicates the fused image generated by our method. This fused image possesses rich texture details and, most importantly, the targets are clearly and distinctly visible, making it highly valuable for further analysis and various applications.
该文件仅包含我方研究结果的微小部分。所展示的特定图像系我方融合方法应用于TNO测试集后所取得的成果。其中,“ir”,即红外光图像,以其丰富的热辐射信息而著称,然而遗憾的是其空间分辨率相对较低。另一方面,代表可见光图像的“vis”,则富含场景信息。然而,其不足之处在于其中的人类目标并不十分清晰可见。‘我方’一词指代由我方方法生成的融合图像。该融合图像具有丰富的纹理细节,更重要的是,目标物清晰且轮廓分明,对于进一步的分析及多种应用具有极高的价值。
提供机构:
ieee-dataport.org



