Generative adversarial networks- and ResNets-based framework for image translation with super-resolution

Hui Hu; Cui Miao; WenWen Hu

doi:10.1117/1.JEI.27.6.063018

11 December 2018 Generative adversarial networks- and ResNets-based framework for image translation with super-resolution

Hui Hu, Cui Miao, WenWen Hu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063018 (December 2018). https://doi.org/10.1117/1.JEI.27.6.063018

Abstract

Recent research on image translation has great progress with the development of generative adversarial networks (GANs) techniques. Generating high-resolution images with unsupervised architecture is one of the most challenging tasks for image translation. To this end, we propose an enhanced super-resolution generative adversarial network for image translation. First, for unlabeled datasets, we employ reconstructed consistency loss and mutual dual GANs, which contains two generators:G_{A → B}, G_{B → A} and two discriminators: D_B, D_A to develop an unsupervised learning framework. As reconstructed consistency loss is added between generators of G_{A → B} and G_{B → A}, our designed overall architecture can learn mapping function of different domains even without unpaired samples. In addition, the generator network includes encoder network, decoder network, and residual network with skip connections to generate high-resolution images with realistic details. Meanwhile, a stable normalization is proposed to stabilize the training of our discriminator networks. Finally, experimental results are carried out on six different datasets, demonstrating that our algorithms outperform the state-of-the-art methods in terms of the image quality and image resolution.

Citation Download Citation

Hui Hu, Cui Miao, and WenWen Hu "Generative adversarial networks- and ResNets-based framework for image translation with super-resolution," Journal of Electronic Imaging 27(6), 063018 (11 December 2018). https://doi.org/10.1117/1.JEI.27.6.063018

Received: 24 July 2018; Accepted: 7 November 2018; Published: 11 December 2018

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