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Development of de-noised image reconstruction technique using Convolutional AutoEncoder for fast monitoring of fuel assemblies
Nuclear Engineering and Technology ( IF 2.7 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.net.2020.08.020
Se Hwan Choi , Hyun Joon Choi , Chul Hee Min , Young Hyun Chung , Jae Joon Ahn

Abstract The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1–2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based de-noising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. Convolutional AutoEncoder (CAE) was employed for de-noising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images; the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image.

中文翻译:

使用卷积自动编码器开发去噪图像重建技术,用于燃料组件的快速监测

摘要 国际原子能机构开发了一种层析成像系统,可在 1-2 h 量级内完成燃料组件的逐棒验证总时间,但对某些燃料类型仍有限制。本研究的目的是开发一种基于深度学习的去噪过程,与传统技术相比,提高燃料组件的断层图像采集速度。卷积自编码器 (CAE) 被用于对滤波反投影 (FBP) 算法重建的低质量图像进行去噪。图像数据集是通过蒙特卡罗方法使用 FBP 和地面实况 (GT) 图像构建的,用于 511 种丢失燃料棒的模式。通过比较GT和FBP图像与GT和CAE图像之间的逐像素减影图像来评估CAE模型的去噪性能;样本图像 1、2 和 3 的像素值的平均差异对于 FBP 图像分别为 7.7%、28.0% 和 44.7%,对于预测图像分别为 0.5%、1.4% 和 1.9%。即使对于无法区分源模式的 FBP 图像,CAE 模型也可以成功地估计与 GT 图像类似的模式。
更新日期:2020-08-01
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