no code implementations • 27 Dec 2023 • Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Kaiyuan Jiang, Zhengmi Tang, Shinichiro Omachi
Conclusions: In this study, we propose a novel framework called $O^{2}$former for radiological image super-resolution tasks, which improves the reconstruction model's performance by introducing an orientation operator and multi-scale feature fusion strategy.
no code implementations • 1 Dec 2023 • Yongsong Huang, Shinichiro Omachi
The ability of generative models to accurately fit data distributions has resulted in their widespread adoption and success in fields such as computer vision and natural language processing.
1 code implementation • 15 Nov 2023 • Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Yafei Dong, Shinichiro Omachi
DASRGAN operates on the synergy of two key components: 1) Texture-Oriented Adaptation (TOA) to refine texture details meticulously, and 2) Noise-Oriented Adaptation (NOA), dedicated to minimizing noise transfer.
no code implementations • 23 Oct 2023 • Yongsong Huang, Wanqing Xie, Mingzhen Li, Mingmei Cheng, Jinzhou Wu, Weixiao Wang, Jane You, Xiaofeng Liu
However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i. e., non-i. i. d.)
1 code implementation • 22 Dec 2022 • Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Shinichiro Omachi
Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks.
1 code implementation • 5 Aug 2022 • Yongsong Huang, Qingzhong Wang, Shinichiro Omachi
To the best of our knowledge, this is the first composite degradation model proposed for radiographic images.
no code implementations • 2 Sep 2021 • Yongsong Huang, Zetao Jiang, Qingzhong Wang, Qi Jiang, Guoming Pang
Recently, deep learning methods have dominated image super-resolution and achieved remarkable performance on visible images; however, IR images have received less attention.