Simulated Artifacts and Data Augmentation for Real-World Video Motion Deblurring

This paper proposes a data augmentation method that simulates artifacts specific to real-world videos as a preprocessing step for applying a deep learning-based video deblurring method to real-world videos. Conventional methods in video deblurring using deep learning have suffered from poor generalization performance. Even if a video deblurring method shows high accuracy on a test dataset in the same domain as the training dataset, it will show less accuracy when inferring real-world test videos. Therefore, we assume that real-world videos contain compression noise and image processing artifacts not included in training deblurring datasets. We introduce a data augmentation method that applies data transformations simulating these real-world video-specific degradations during training. In this study, we prepare a real-world test dataset with no ground truth videos using video captured by a commercially available smartphone. Then, we aim to improve the estimation accuracy of deblurring in real-world videos by performing inference using our data augmentation method.