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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