Kernel Adversarial Learning for Real-world Image Super-resolution

• URL: http://arxiv.org/abs/2104.09008v3
• Date: Thu, 5 Sep 2024 09:15:34 GMT
• Title: Kernel Adversarial Learning for Real-world Image Super-resolution
• Authors: Hu Wang, Congbo Ma, Jianpeng Zhang, Wei Emma Zhang, Gustavo Carneiro,
• Abstract summary: 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 framework. In the proposed framework, degradation kernels and noises are adaptively modelled rather than explicitly specified. We also propose a high-frequency selective objective and an iterative supervision process to further boost the model SR reconstruction accuracy.
• Score: 23.904933824966605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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