Search Results for author: Tomo Miyazaki

Found 12 papers, 7 papers with code

Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model

1 code implementation27 May 2024 Shoma Iwai, Tomo Miyazaki, Shinichiro Omachi

A critical obstacle of these generative NIC methods is that each model is optimized for a single bit rate.

Image Compression

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

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

A Scene-Text Synthesis Engine Achieved Through Learning from Decomposed Real-World Data

1 code implementation6 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.

Image Generation

Stroke-Based Scene Text Erasing Using Synthetic Data for Training

1 code implementation23 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.

Image Inpainting Text Detection

Structural Data Recognition with Graph Model Boosting

no code implementations8 Mar 2017 Tomo Miyazaki, Shinichiro Omachi

This paper presents a novel method for structural data recognition using a large number of graph models.

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