Kernel Adversarial Learning for Real-world Image Super-resolution
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| _version_ | 1866913491595558912 |
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| author | Wang, Hu Ma, Congbo Zhang, Jianpeng Zhang, Wei Emma Carneiro, Gustavo |
| author_facet | Wang, Hu Ma, Congbo Zhang, Jianpeng Zhang, Wei Emma Carneiro, Gustavo |
| contents | Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and additive noises. However, these techniques only assume crude approximations of the real-world image degradation process, which should involve complex kernels and noise patterns that are difficult to model using simple assumptions. In this paper, we propose a more realistic process to synthesise low-resolution images for real-world image SR by introducing a new Kernel Adversarial Learning Super-resolution (KASR) framework. In the proposed framework, degradation kernels and noises are adaptively modelled rather than explicitly specified. Moreover, we also propose a high-frequency selective objective and an iterative supervision process to further boost the model SR reconstruction accuracy. Extensive experiments validate the effectiveness of the proposed framework on real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_09008 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Kernel Adversarial Learning for Real-world Image Super-resolution Wang, Hu Ma, Congbo Zhang, Jianpeng Zhang, Wei Emma Carneiro, Gustavo Computer Vision and Pattern Recognition Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and additive noises. However, these techniques only assume crude approximations of the real-world image degradation process, which should involve complex kernels and noise patterns that are difficult to model using simple assumptions. In this paper, we propose a more realistic process to synthesise low-resolution images for real-world image SR by introducing a new Kernel Adversarial Learning Super-resolution (KASR) framework. In the proposed framework, degradation kernels and noises are adaptively modelled rather than explicitly specified. Moreover, we also propose a high-frequency selective objective and an iterative supervision process to further boost the model SR reconstruction accuracy. Extensive experiments validate the effectiveness of the proposed framework on real-world datasets. |
| title | Kernel Adversarial Learning for Real-world Image Super-resolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2104.09008 |