Kernel Adversarial Learning for Real-world Image Super-resolution

Hu Wang; Congbo Ma; Jianpeng Zhang; Wei Emma Zhang; Gustavo Carneiro

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arXiv · 2104.09008

Kernel Adversarial Learning for Real-world Image Super-resolution

Hu Wang·Congbo Ma·Jianpeng Zhang·Wei Emma Zhang·Gustavo Carneiro

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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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BibTeX RIS

Hu Wang, Congbo Ma, Jianpeng Zhang, Wei Emma Zhang, Gustavo Carneiro. 2021-04-19. Kernel Adversarial Learning for Real-world Image Super-resolution. https://arxiv.org/abs/2104.09008

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