Stage-GAN with Semantic Maps for Large-scale Image Super-resolution

• Zhensong Wei,
• Huihui Bai,
• Yao Zhao,

Vol. 13, No. 8, August 30, 2019
10.3837/tiis.2019.08.007, Download Paper (Free):

• super-resolution
• Stage-GAN
• generative adversarial networks
• Semantic maps
• Large scaling factors

Abstract

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ×8, our method performs favorably against other methods in terms of gradients similarity.

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