EnlightenGAN: Deep Light Enhancement Without Paired Supervision

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challeng...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 30; S. 2340 - 2349
Hauptverfasser:	Jiang, Yifan, Gong, Xinyu, Liu, Ding, Cheng, Yu, Fang, Chen, Shen, Xiaohui, Yang, Jianchao, Zhou, Pan, Wang, Zhangyang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Gallium nitride Generative adversarial networks Ground truth Image enhancement Image restoration Light Lighting Low-light enhancement Training Training data unsupervised learning Visualization
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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