Towards Unsupervised Deep Image Enhancement With Generative Adversarial Network

Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to learn an automatic photo enhancer for paired data, which consists of low-quality photos and corresponding expert-retouched...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 29; S. 9140 - 9151
Hauptverfasser:	Ni, Zhangkai, Yang, Wenhan, Wang, Shiqi, Ma, Lin, Kwong, Sam
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Gallium nitride generative adversarial network Generative adversarial networks Generators global attention Histograms Image color analysis Image enhancement Image quality Machine learning Mapping Regularization Task analysis Unsupervised learning
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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