Abstract
In this paper, we proposed a method to synthesizing a super-resolution image with the given image and text descriptions. Our work contains two parts. Wasserstein GAN is used to generate low-level resolution image under the guidance of a novel loss function. Then, a convolution net is followed to refine the resolution. This is an end-to-end network architecture. We have validated our model on Caltech-200 bird dataset, Oxford-102 flower dataset, and BSD300 dataset. The experiments show that the generated images not only match the given descriptions well but also maintain detailed features of original images with a higher resolution.
Han and Zhang—Equal contribution.
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Han, J., Zhang, Z., Mao, A., Zhou, Y. (2020). Semantics Images Synthesis and Resolution Refinement Using Generative Adversarial Networks. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_74
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DOI: https://doi.org/10.1007/978-981-13-6504-1_74
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