Underwater image enhancement based on conditional generative adversarial network

Underwater images play an essential role in acquiring and understanding underwater information. High-quality underwater images can guarantee the reliability of underwater intelligent systems. Unfortunately, underwater images are characterized by low contrast, color casts, blurring, low light, and un...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Signal processing. Image communication Jg. 81; S. 115723
Hauptverfasser:	Yang, Miao, Hu, Ke, Du, Yixiang, Wei, Zhiqiang, Sheng, Zhibin, Hu, Jintong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.02.2020 Elsevier BV
Schlagworte:	Adversarial learning Blurring Conditional generative adversarial networks Deep learning Generative adversarial networks Image acquisition Image contrast Image enhancement Image quality Machine learning Underwater Underwater image enhancement Deep learning Underwater image enhancement Conditional generative adversarial networks Adversarial learning
ISSN:	0923-5965, 1879-2677
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!