Yuzhen Xiao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yuzhen Xiao has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jianing You
China Agricultural University
1 shared publication - Jidong Zhang
Yantai Research Institute, China Agricultural University, Yantai 264670, China
1 shared publication - Qing Wang
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
1 shared publication - Wei Chen
School of Art and Media, Century College, Beijing University of Posts and Telecommunications, Beijing 102100, China
1 shared publication - Yichen Wang
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
SR-DeblurUGAN: An End-to-End Super-Resolution and Deblurring Model with High Performance
In this paper, we consider the difference in the abstraction level of features extracted by different perceptual layers and use a weighted perceptual loss-based generative adversarial network to deblur the UAV images, which removes the blur and restores the texture details of the images well. The perceptual loss is used as an objective evaluation index for training process monitoring and model selection, which eliminates the need for extensive manual comparison of the deblurring effect and facilitates model selection. The UNet jump connection structure facilitates the transfer of features across layers in the network, reduces the learning difficulty of the generator, and improves the stability of adversarial training.
Open AccessSR-DeblurUGAN: An End-to-End Super-Resolution and Deblurring Model with High Performance
In this paper, we consider the difference in the abstraction level of features extracted by different perceptual layers and use a weighted perceptual loss-based generative adversarial network to deblur the UAV images, which removes the blur and restores the texture details of the images well. The perceptual loss is used as an objective evaluation index for training process monitoring and model selection, which eliminates the need for extensive manual comparison of the deblurring effect and facilitates model selection. The UNet jump connection structure facilitates the transfer of features across layers in the network, reduces the learning difficulty of the generator, and improves the stability of adversarial training.