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Convolutional Transformer-based Deblurring Model for X-ray Images
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- 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
- 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