17 citations · 55 across the 21 of their papers we have counts for
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Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer
Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu +3
Background and objective: High-resolution radiographic images play a pivotal role in the early diagnosis and treatment of skeletal muscle-related diseases. It is promising to enhan…
Infrared Image Super-Resolution via GAN
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 p…
Texture and Noise Dual Adaptation for Infrared Image Super-Resolution
Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu +2
Recent efforts have explored leveraging visible light images to enrich texture details in infrared (IR) super-resolution. However, this direct adaptation approach often becomes a d…
Infrared Image Super-Resolution: Systematic Review, and Future Trends
Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu +1
Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a…
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN
Yongsong Huang, Qingzhong Wang, Shinichiro Omachi
In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medic…
Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks
Shoma Iwai, Tomo Miyazaki, Yoshihiro Sugaya +1
We propose a GAN-based image compression method working at extremely low bitrates below 0.1bpp. Most existing learned image compression methods suffer from blur at extremely low bi…