Hongyin Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hongyin Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Changhong Liu
Guangzhou University
1 shared publication - Run-Yang Zhong
The University of Hong Kong
1 shared publication - Shaohu Peng
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication - Yier Yan
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China
1 shared publication - Yongjun Zhang
School of Electro-Mechanical Engineering, Guangdong University of Technology, No. 100 West Outer Ring Road, Guangzhou Higher Education Mega Center, Panyu District, Guangzhou 510006, China
1 shared publication - Zhongwei Liang
Guangzhou University
1 shared publication
Latest Publications
- Open Access
- 24 September 2022
- Published by MDPI AG in Electronics
- Vol. 11 (19) , 3038
A Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution
The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.
- 24 September 2022
- Published by MDPI AG in Electronics
- Vol. 11 (19) , 3038
Open AccessA Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution
The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.