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.
翻译:目前的深层图像超分辨率(SR)方法试图从下摄图像中恢复高分辨率图像,或假设从简单的高森内核和添加剂噪音中降解高分辨率图像,然而,这种简单的图像处理技术代表了降低图像分辨率的现实世界程序的粗略近似值。在本文件中,我们提出一个更现实的降低图像分辨率的过程,方法是引入一个新的内核对立学习超分辨率(KASR)框架来处理真实世界图像SR问题。在拟议的框架中,降解内核和噪音是适应性模型而不是明确规定的。此外,我们还提议了一个迭代监督过程和高频选择目标,以进一步提升SR重建模型的准确性。广泛的实验验证了拟议中真实世界数据集框架的有效性。