Search Results for author: Yongsong Huang

Found 7 papers, 3 papers with code

Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer

no code implementations27 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.

Denoising Image Enhancement +1

Infrared Image Super-Resolution via GAN

no code implementations1 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.

Image Super-Resolution Infrared image super-resolution

Target-oriented Domain Adaptation for Infrared Image Super-Resolution

1 code implementation15 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.

Domain Adaptation Image Super-Resolution +1

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

no code implementations23 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.)

Cardiac Segmentation Data Augmentation +2

Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN

1 code implementation5 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.

Denoising Generative Adversarial Network +1

Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN

no code implementations2 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.

Image Super-Resolution Infrared image super-resolution

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