Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution

• URL: http://arxiv.org/abs/2009.03693v1
• Date: Mon, 7 Sep 2020 11:11:18 GMT
• Title: Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution
• Authors: Rao Muhammad Umer, Christian Micheloni
• Abstract summary: We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions. We propose the Super-Resolution Residual Cyclic Generative Adversarial Network (SRResCycGAN) by training with a generative adversarial network (GAN) framework for the LR to HR domain translation.
• Score: 20.537597542144916
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recent deep learning based single image super-resolution (SISR) methods mostly train their models in a clean data domain where the low-resolution (LR) and the high-resolution (HR) images come from noise-free settings (same domain) due to the bicubic down-sampling assumption. However, such degradation process is not available in real-world settings. We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions, which is inspired by the recent success of CycleGAN in the image-to-image translation applications. We propose the Super-Resolution Residual Cyclic Generative Adversarial Network (SRResCycGAN) by training with a generative adversarial network (GAN) framework for the LR to HR domain translation in an end-to-end manner. We demonstrate our proposed approach in the quantitative and qualitative experiments that generalize well to the real image super-resolution and it is easy to deploy for the mobile/embedded devices. In addition, our SR results on the AIM 2020 Real Image SR Challenge datasets demonstrate that the proposed SR approach achieves comparable results as the other state-of-art methods.

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