Development of de-noised image reconstruction technique using Convolutional AutoEncoder for fast monitoring of fuel assemblies

Se Hwan Choi, Hyun Joon Choi, Chul Hee Min, Young Hyun Chung, Jae Joon Ahn

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)888-893
Number of pages6
JournalNuclear Engineering and Technology
Issue number3
Publication statusPublished - 2021 Mar

Bibliographical note

Funding Information:
This research was supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety (KoFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 1803027 ).

Publisher Copyright:
© 2020

All Science Journal Classification (ASJC) codes

  • Nuclear Energy and Engineering


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