从异质内容协同感知视角重新思考红外和可见图像融合
Minxian Shen1, Gongrui Huang1, Mingye Ju1
1School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210000, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
HCSPNet是一个新的生成对抗网络 (GAN) 框架,通过使用异构的双重区分器来增强红外和可见图像融合 (IVIF). 这种方法减少了文物,并保留了这两种图像类型的关键细节.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 红外和可见图像融合 (IVIF) 结合了热和纹理细节,以获得更丰富的输出.
- 生成对抗性网络 (GAN) 对IVIF有前途,但由于同质歧视因素,它们往往会遭受人工物损害.
研究的目的:
- 引入HCSPNet,这是一个基于GAN的新型框架,旨在克服现有IVIF方法的局限性.
- 为了提高质量和减少合红外和可见图像中的工件.
主要方法:
- HCSPNet使用异构的双重分辨器,每个分辨器都针对红外和可见图像属性而定制.
- 适应突出信息蒸 (ASID) 模块集成到分辨器中,以关注关键的热和纹理细节.
- 该框架旨在提供强大的信息保存,即使在图像退化的情况下.
主要成果:
- 实验证明了HCSPNet在多个数据集中的卓越性能和概括能力.
- 提出的异质双分辨器有效地减少了融合图像中的不需要的工件.
- 不同质的双重区分器作为一个插即用模块来增强现有的基于GAN的融合方法.
结论:
- 在红外和可见图像融合方面,HCSPNet提供了显著的进步.
- 该框架的设计确保了高质量的融合图像,保存了细节和最小的文物.
- 不同质的双分辨器方法在图像融合中具有实际应用的潜力.
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