基于语义指导和视觉感知的红外可见图像融合
Xiaoyu Chen1, Zhijie Teng1, Yingqi Liu1
1Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了SGVPGAN,这是一个先进的红外可见图像融合框架. 它通过提高目标突出度和视觉感知来增强智能车辆的夜视.
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:
- 红外可见融合对于智能汽车的夜视非常重要.
- 现有的融合方法在目标突出性和视觉感知方面扎.
- 缺乏有效的融合规则导致对比度差,目标可见性差.
研究的目的:
- 提出SGVPGAN,一个高质量的红外可见图像融合的对抗框架.
- 为了增强合图像中的目标突出显示和自然视觉外观.
- 为了提高智能汽车的夜视能力.
主要方法:
- 开发了SGVPGAN框架与对抗语义指导 (ASG) 和对抗视觉感知 (AVP) 模块.
- ASG模块转移用于目标突出显示的语义.
- AVP模块分析视觉特征,以指导适应性重量地图生成以获得自然外观.
主要成果:
- 该ASG和AVP模块有效指导融合过程.
- 保存可见图像中的细节和红外图像中的突出信息.
- SGVPGAN显示出与现有融合方法相比的显著改进.
结论:
- 拟议的SGVPGAN框架成功地增强了红外可见图像融合.
- 在目标突出性和视觉自然性方面实现了卓越的性能.
- 为智能汽车夜视系统提供了巨大的潜力.
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