工业正子子图像与生成网络的代重建
Mingwei Zhu1, Min Zhao2, Min Yao2
1School of Mechanical Engineering, Nantong University, Nantong, Jiangsu, China.
PloS one
|November 19, 2025
概括
我们开发了一种使用生成对抗网络 (PIIR-GAN) 的新正子成像重建方法. 这种方法提高了工业非破坏性测试的图像质量,特别是有限的数据,改善了缺陷检测.
科学领域:
- 工业成像技术的产业成像技术
- 非破坏性测试 不破坏性测试
- 图像重建 图像的重建
背景情况:
- 定子成像为检查工业部件内部结构提供了高灵敏度.
- 重建高质量的正子图像是很困难的,因为采样有限和错误的反向问题,阻碍了应用程序,如闭腔检测.
研究的目的:
- 为工业正子子成像提出先进的代重建方法.
- 为了提高图像质量和缺陷检测能力,特别是在低样本条件下.
主要方法:
- 开发了一个正子成像代重建生成对抗网络 (PIIR-GAN).
- 整合了一个生成的对抗框架与一个自我注意机制,用于事先的信息利用.
- 在端到端学习的代重建过程中嵌入神经网络,并纳入基于概率的约束.
主要成果:
- 与GATE模拟中的传统方法相比,PIIR-GAN在峰值信号与噪声比率 (PSNR) 和结构相似度指数 (SSIM) 中显著改善.
- 该方法的有效性通过现实世界的工业缺陷检测得到验证,证实了其实际实用性.
结论:
- 拟议的PIIR-GAN方法有效地提高了工业正子图像质量,特别是在有限的采样条件下.
- 这种先进的重建技术增强了对工业应用的非破坏性测试的能力.
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