Image super-resolution based on conditional generative adversarial network

Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high-frequency details. However, a mismatch between the input and the output may arise when GAN is directly applied to image super-resolution. To alleviate this issue, the authors adopte...

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Veröffentlicht in:	IET image processing Jg. 14; H. 13; S. 3006 - 3013
Hauptverfasser:	Gao, Hongxia, Chen, Zhanhong, Huang, Binyang, Chen, Jiahe, Li, Zhifu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.11.2020
Schlagworte:	adversarial loss term cGAN discriminator conditional GAN conditional generative adversarial network generator loss function high‐frequency information high‐resolution image HR images image resolution image super‐resolution L1 loss term low‐frequency information low‐resolution image neural nets Research Article unsupervised learning adversarial loss term conditional GAN high-frequency information conditional generative adversarial network high-resolution image unsupervised learning HR images generator loss function cGAN discriminator L1 loss term low-frequency information image resolution neural nets image super-resolution low-resolution image
ISSN:	1751-9659, 1751-9667
Online-Zugang:	Volltext
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