基于神经网络的大孔径厚针孔成像复原算法研究

李冬; 盛亮; 李阳; 段宝军

doi:10.11884/HPLPB202234.210345

基于神经网络的大孔径厚针孔成像复原算法研究

doi: 10.11884/HPLPB202234.210345

西北核技术研究所强脉冲辐射环境模拟与效应国家重点实验室, 西安 710024

基金项目: 卓越青年基金项目(JQZQ021901)

详细信息

作者简介:
李冬，742944723@qq.com

中图分类号: TL99
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-09
- 修回日期: 2021-12-21
- 网络出版日期: 2021-12-13
- 刊出日期: 2022-06-15

Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network

State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China

摘要

摘要: 为了更好地获取低强度辐射源空间分布图像，提出一种使用神经网络算法将大孔径厚针孔退化图像复原的方法。建立了孔径5 mm、10 mm、15 mm的厚针孔模型，获得了3600个汉字形状辐射源的厚针孔退化图像集。基于DnCNN神经网络模型，建立了大孔径厚针孔退化图像复原神经网络，并与维纳滤波、Lucy-Richardson这些传统算法进行了比较。在考虑噪声影响后，利用迁移学习理论，对原神经网络模型进行迁移训练，再对含噪大孔径厚针孔退化图像进行复原。神经网络算法复原的RMSE明显低于传统方法，迁移学习显著减小了噪声的影响。证明了神经网络算法在大孔径厚针孔退化图像复原领域的优越性，并验证了神经网络方法复原含噪大孔径厚针孔退化图像的可行性。
- 大孔径厚针孔 /
- 神经网络 /
- 图像复原 /
- 迁移学习
Abstract: To obtain the spatial distribution image of low intensity radiation source better, a method is proposed to restore large aperture thick pinhole degraded image using neural network algorithm. The thick pinhole model of 5 mm, 10 mm and 15 mm apertures is established, and the degenerate image sets of thick pinhole for the shape radiation source of 3600 Chinese characters are obtained. Based on the DnCNN neural network model, the neural network for image restoration with large aperture and thick pinhole is obtained, and compared with traditional algorithms such as Wiener filter and Lucy Richardson. After considering the influence of noise, the original neural network model is trained by means of transfer learning theory, and then the degraded image of large aperture pinhole with noise is restored. The RMSE of neural network algorithm is significantly lower than that of the traditional one, and the effect of noise is greatly improved by transfer learning. This paper proves the superiority of neural network algorithm in the field of image restoration with large aperture and thick pinhole, and verifies the feasibility of neural network method to restore the large aperture thick pinhole degraded image with noise.
- large thick aperture /
- neural network /
- image reconstruction /
- transfer learning

HTML全文

图 1 模拟厚针孔模型

Figure 1. Thick pinhole simulation model

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图 2 蒙特卡罗软件结果

Figure 2. Monte Carlo software results

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图 3 DnCNN网络结构图

Figure 3. Diagram of DnCNN network structure

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图 4 训练结果

Figure 4. Training results

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图 5 维纳滤波复原结果

Figure 5. Wiener filter restoration result

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图 6 L-R算法复原结果

Figure 6. L-R algorithm restoration results

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图 7 10mm孔径厚针孔退化图像添加噪声

Figure 7. 10 mm aperture thick pinhole degraded image adds noise

下载: 全尺寸图片幻灯片

图 8 神经网复原结果

Figure 8. Neural network restoration results

下载: 全尺寸图片幻灯片

表 1 测试数据平均RMSE

Table 1. RMSE of test data

aperture/mm	average RMSE
5	16.6067
10	30.4662
15	35.3384

下载: 导出CSV

表 2 测试数据平均RMSE

Table 2. Comparison of average RMSE of test data

aperture/mm	average RMSE
aperture/mm	Wiener filtering	Lucy-Richardson	neural network
5	46.654 6	48.316 8	16.781 9
10	48.873 6	50.161 2	31.144 2
15	50.613 4	50.845 5	36.294 7
注：加粗字体为每行最优值。

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参考文献(12)

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