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 • 19 May 2023 • Shohei Uchigasaki, Tomo Miyazaki, Shinichiro Omachi
We developed a scene text image quality assessment model to assess text quality in compressed images.
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 • 6 Sep 2022 • Zhengmi Tang, Tomo Miyazaki, Shinichiro Omachi
Some of these studies have proposed generating scene-text images through learning; however, owing to the absence of a suitable training dataset, unsupervised frameworks have been explored to learn from existing real-world data, which might not yield reliable performance.
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.
1 code implementation • 23 Apr 2021 • Zhengmi Tang, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi
To compensate for the lack of pairwise real-world data, we made considerable use of synthetic text after additional enhancement and subsequently trained our model only on the dataset generated by the improved synthetic text engine.
1 code implementation • 24 Aug 2020 • Shoma Iwai, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi
To address both of the drawbacks, our method adopts two-stage training and network interpolation.
no code implementations • 16 Apr 2020 • Huy Manh Nguyen, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi
A single space is not enough to accommodate various videos and sentences.
no code implementations • 8 Mar 2017 • Tomo Miyazaki, Shinichiro Omachi
This paper presents a novel method for structural data recognition using a large number of graph models.
no code implementations • 20 Jan 2017 • Tomo Miyazaki, Tatsunori Tsuchiya, Yoshihiro Sugaya, Shinichiro Omachi, Masakazu Iwamura, Seiichi Uchida, Koichi Kise
The proposed method uses strokes from given samples for font generation.