Attention‐generative adversarial networks for simulating rain field
Autor: | Chen Li, Zheng Yang Zhao, Jia Li, Ye Cai Guo |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2024 |
Předmět: | |
Zdroj: | IET Image Processing, Vol 18, Iss 6, Pp 1540-1549 (2024) |
Druh dokumentu: | article |
ISSN: | 1751-9667 1751-9659 |
DOI: | 10.1049/ipr2.13047 |
Popis: | Abstract The synthesis of rain fields is essential in multiple research fields and applications, including Single‐image Derain. However, there is a lack of research on simulated rain fields, and the existing rain field generation models struggle to capture complex spatial distributions and generate truly random rain fields. To address this, the authors propose a generative adversarial networks‐based rain field generation network, which consists of a generator, a discriminator, and a feature extraction block that can produce realistic and complex rain fields. The authors’ experiments demonstrate that this method achieves an average Frechet Inception Distance score of 0.035, and user studies indicate that the generated rain distribution looks naturally. |
Databáze: | Directory of Open Access Journals |
Externí odkaz: |
načítá se...