1 code implementation • 14 Jun 2023 • Zhengyong Wang, Liquan Shen, Yihan Yu, Yuan Hui
With performing region-wise feature learning for regions with different quality separately, the network provides an effective guidance for global features and thus guides intra-image differentiated enhancement.
1 code implementation • 22 Aug 2021 • Zhengyong Wang, Liquan Shen, Mei Yu, Kun Wang, Yufei Lin, Mai Xu
However, these methods ignore the significant domain gap between the synthetic and real data (i. e., interdomain gap), and thus the models trained on synthetic data often fail to generalize well to real underwater scenarios.
no code implementations • 20 Aug 2021 • Zhengyong Wang, Liquan Shen, Mei Yu, Yufei Lin, Qiuyu Zhu
The proposed framework includes an analysis network and a synthesis network, one for priors exploration and another for priors integration.
3 code implementations • 10 Jun 2019 • Qiuyu Zhu, Zhengyong Wang
The algorithm uses PEDCC (Predefined Evenly-Distributed Class Centroids) as the clustering centers, which ensures the inter-class distance of latent features is maximal, and adds data distribution constraint, data augmentation constraint, auto-encoder reconstruction constraint and Sobel smooth constraint to improve the clustering performance.