Degradation model learning for real-world single image super-resolution

Abstract

It is well-known that the single image super-resolution (SISR) models trained on those synthetic datasets, where a low-resolution (LR) image is generated by applying a simple degradation operator (e.g., bicubic downsampling) to its high-resolution (HR) counterpart, have limited generalization capability on real-world LR images, whose degradation process is much more complex. Several real-world SISR datasets have been constructed to reduce this gap; however, their scale is relatively small due to laborious and costly data collection process. To remedy this issue, we propose to learn a realistic degradation model from the existing real-world datasets, and use the learned degradation model to synthesize realistic HR-LR image pairs. Specifically, we learn a group of basis degradation kernels, and simultaneously learn a weight prediction network to predict the pixel-wise spatially variant degradation kernel as the weighted combination of the basis kernels. With the learned degradation model, a large number of realistic HR-LR pairs can be easily generated to train a more robust SISR model. Extensive experiments are performed to quantitatively and qualitatively validate the proposed degradation learning method and its effectiveness in improving the generalization performance of SISR models in practical scenarios.