A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution

• URL: http://arxiv.org/abs/2107.03145v1
• Date: Wed, 7 Jul 2021 11:15:17 GMT
• Title: A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution
• Authors: Rao Muhammad Umer, Asad Munir, Christian Micheloni
• Abstract summary: Super-Resolution Residual StarGAN (SR2*GAN) is a novel and scalable approach that super-resolves the LR images for the multiple LR domains using only a single model. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments compared to other state-of-the-art methods.
• Score: 21.39772242119127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, most of state-of-the-art single image super-resolution (SISR) methods have attained impressive performance by using deep convolutional neural networks (DCNNs). The existing SR methods have limited performance due to a fixed degradation settings, i.e. usually a bicubic downscaling of low-resolution (LR) image. However, in real-world settings, the LR degradation process is unknown which can be bicubic LR, bilinear LR, nearest-neighbor LR, or real LR. Therefore, most SR methods are ineffective and inefficient in handling more than one degradation settings within a single network. To handle the multiple degradation, i.e. refers to multi-domain image super-resolution, we propose a deep Super-Resolution Residual StarGAN (SR2*GAN), a novel and scalable approach that super-resolves the LR images for the multiple LR domains using only a single model. The proposed scheme is trained in a StarGAN like network topology with a single generator and discriminator networks. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments compared to other state-of-the-art methods.

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