Kernel Adversarial Learning for Real-world Image Super-resolution
Current deep image super-resolution (SR) approaches attempt to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and additive noises. However, such simple image processing techniques represent crude approximations of the real-world procedure of lowering image resolution. In this paper, we propose a more realistic process to lower image resolution by introducing a new Kernel Adversarial Learning Super-resolution (KASR) framework to deal with the real-world image SR problem. In the proposed framework, degradation kernels and noises are adaptively modeled rather than explicitly specified. Moreover, we also propose an iterative supervision process and high-frequency selective objective 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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