  Recent deep-learning based Super-Resolution (SR) methods have achieved
remarkable performance on images with known degradation. However, these methods
always fail in real-world scene, since the Low-Resolution (LR) images after the
ideal degradation (e.g., bicubic down-sampling) deviate from real source
domain. The domain gap between the LR images and the real-world images can be
observed clearly on frequency density, which inspires us to explictly narrow
the undesired gap caused by incorrect degradation. From this point of view, we
design a novel Frequency Consistent Adaptation (FCA) that ensures the frequency
domain consistency when applying existing SR methods to the real scene. We
estimate degradation kernels from unsupervised images and generate the
corresponding LR images. To provide useful gradient information for kernel
estimation, we propose Frequency Density Comparator (FDC) by distinguishing the
frequency density of images on different scales. Based on the domain-consistent
LR-HR pairs, we train easy-implemented Convolutional Neural Network (CNN) SR
models. Extensive experiments show that the proposed FCA improves the
performance of the SR model under real-world setting achieving state-of-the-art
results with high fidelity and plausible perception, thus providing a novel
effective framework for real-world SR application.
