Real-World Super-Resolution Based on Iterative Frequency Domain Degradation Model

Abstract Single-image super-resolution (SR) methods often encounter difficulties when applied to real-worldimages because of deviations between the degradation model and real-world degradation distribution. Recent studies have attempted to address this issue by adopting more complex degradation models tosimulate real-world degradation distributions. However, these methods tend to produce over-smoothedresults lacking fine-grained details. Furthermore, these methods neglect the disparity between syntheticand real-world images in the frequency domain. This study proposes a more practical and detail-oriented degradation model for real-world image SR. The proposed model employs aliasing estimationand an iterative frequency domain degradation algorithm to narrow the frequency domain gap between synthetic and real-world images, thus expanding the latent degradation distribution. The proposedmodel considers the impact of various factors such as camera shake, defocus, and compression onthe image, effectively simulating the complex and diverse degradation processes in the real world.Experiments demonstrate that combining the proposed degradation model with existing SR methods achieves excellent image perceptual quality on real-world images.