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基于改进的残余网络的静电断层扫描的图像重建
Xianglong Liu1, Danyang Li1, Ying Wang2
1School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
The Review of scientific instruments
|June 4, 2024
概括
本研究介绍了一种改进的ResNet-34网络 (P-ResNet) 用于静电断层扫描 (EST) 图像重建. P-ResNet模型表现出高精度和强大的抗噪能力,在EST任务中表现优于传统算法.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 应用物理 应用物理
背景情况:
- 静电断层扫描 (EST) 依赖于解决反向问题,由于与所需的重建细节相比,测量数据有限,这些问题具有挑战性.
- 传统的EST方法与反向问题的不良性质作斗争,导致显著的重建困难.
研究的目的:
- 开发一种先进的深度学习模型,用于在静电断层扫描中准确和强大的图像重建.
- 增强EST重建模型的非线性表达和概括能力.
主要方法:
- 设计了一种改进的ResNet-34网络 (P-ResNet),具有特定的剩余区块配置 (3, 4, 4, 3) 并在每个剩余区块中第二次卷积后集成ReLU激活.
- 引入L2调节损失函数是为了改善模型的概括性.
- 进行了广泛的模拟 (15,930个样本) 和使用新型小型传感器进行试验,用于诱导电荷测量.
主要成果:
- 在200次代后,P-ResNet模型在EST图像重建任务中实现了高精度.
- 该模型在测试不同级别的高斯白噪声时显示出显著的反噪声能力.
- 与传统算法相比,图像相关系数更高,实验结果验证了模拟结果.
结论:
- 拟议的P-ResNet模型为静电断层扫描图像重建提供了一个高效和可通用的解决方案.
- 集成增强的网络架构和规范化技术显著提高了重建性能和对噪声的稳定性.
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