The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cette lettre présente une métrique d’évaluation de la qualité d’image (IQA) pour les images de microscopie électronique à balayage (MEB) basée sur l’inpainting de texture. Inspiré par l'observation selon laquelle les informations de texture des images SEM sont assez sensibles aux distorsions, un réseau d'inpainting de texture est d'abord formé pour extraire les caractéristiques de texture. Ensuite, les poids du réseau d'inpainting de texture entraîné sont transférés au réseau IQA pour l'aider à apprendre une représentation de texture efficace de l'image déformée. Enfin, un réglage fin supervisé est effectué sur le réseau IQA pour prédire le score de qualité de l'image. Les résultats expérimentaux sur l’ensemble de données de qualité d’image SEM démontrent les avantages de la méthode présentée.
Zhaolin LU
China University of Mining and Technology
Ziyan ZHANG
China University of Mining and Technology
Yi WANG
Jiangsu Normal University Kewen College
Liang DONG
China University of Mining and Technology
Song LIANG
China University of Mining and Technology
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Zhaolin LU, Ziyan ZHANG, Yi WANG, Liang DONG, Song LIANG, "SEM Image Quality Assessment Based on Texture Inpainting" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 2, pp. 341-345, February 2021, doi: 10.1587/transinf.2020EDL8123.
Abstract: This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that the texture information of SEM images is quite sensitive to distortions, a texture inpainting network is first trained to extract texture features. Then the weights of the trained texture inpainting network are transferred to the IQA network to help it learn an effective texture representation of the distorted image. Finally, supervised fine-tuning is conducted on the IQA network to predict the image quality score. Experimental results on the SEM image quality dataset demonstrate the advantages of the presented method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8123/_p
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@ARTICLE{e104-d_2_341,
author={Zhaolin LU, Ziyan ZHANG, Yi WANG, Liang DONG, Song LIANG, },
journal={IEICE TRANSACTIONS on Information},
title={SEM Image Quality Assessment Based on Texture Inpainting},
year={2021},
volume={E104-D},
number={2},
pages={341-345},
abstract={This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that the texture information of SEM images is quite sensitive to distortions, a texture inpainting network is first trained to extract texture features. Then the weights of the trained texture inpainting network are transferred to the IQA network to help it learn an effective texture representation of the distorted image. Finally, supervised fine-tuning is conducted on the IQA network to predict the image quality score. Experimental results on the SEM image quality dataset demonstrate the advantages of the presented method.},
keywords={},
doi={10.1587/transinf.2020EDL8123},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - SEM Image Quality Assessment Based on Texture Inpainting
T2 - IEICE TRANSACTIONS on Information
SP - 341
EP - 345
AU - Zhaolin LU
AU - Ziyan ZHANG
AU - Yi WANG
AU - Liang DONG
AU - Song LIANG
PY - 2021
DO - 10.1587/transinf.2020EDL8123
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2021
AB - This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that the texture information of SEM images is quite sensitive to distortions, a texture inpainting network is first trained to extract texture features. Then the weights of the trained texture inpainting network are transferred to the IQA network to help it learn an effective texture representation of the distorted image. Finally, supervised fine-tuning is conducted on the IQA network to predict the image quality score. Experimental results on the SEM image quality dataset demonstrate the advantages of the presented method.
ER -