ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Conference Paper Convolutional Transformer-based Deblurring Model for X-ray Images
Cited 0 time in scopus Share share facebook twitter linkedin kakaostory
Authors
HyunYong Lee, Nac-Woo Kim, Jungi Lee, Seok-Kap Ko
Issue Date
2023-06
Citation
International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2023, pp.834-839
Publisher
IEEE
Language
Korean
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ITC-CSCC58803.2023.10212709
Abstract
Image deblurring is an important pre-processing for improving relevant computer vision tasks. In this paper, we are interested in conducting deblurring X-ray images. Using a convolutional transformer as the main building block, we build an AutoEncoder-style deblurring model for X-ray images. From the experiments using the public X-ray image dataset, we show that our model conducts the deblurring operation well. For example, in terms of structural similarity (SSIM) as a performance metric, our model improves SSIM by up to 27% compared to the blurry images.
KSP Keywords
Building block, Computer Vision(CV), Image datasets, Image deblurring, Main building, Pre-processing, Structure Similarity Index measure(SSIM), performance metrics, transformer-based, x-ray image