REAL-WORLD IMAGE SUPER-RESOLUTION VIA KERNEL AUGMENTATION AND STOCHASTIC VARIATION

Deep learning (DL) based single image super-resolution (SISR) algorithms have now achieved highly satisfactory evaluation and visualization results on synthetic datasets. However, in some practical applications, especially when restoring some real-world low-resolution (LR) photos, the limitation and unicity of the most commonly used bicubic down-sampling kernel often lead to significant performance degradation of models trained under ideal conditions. Thus, we first propose a kernel augmentation (KA) strategy based on generative adversarial networks (GANs) to improve the generalization ability and robustness of current SISR models. Then, we intend to reconstruct the stochastic variation (SV) features that are widely present in natural images to obtain a more realistic feature representation. In the end, extensive experiments demonstrate the feasibility and effectiveness of our approach in dealing with real-world SISR problems.