Fast Ultra High-Definition Video Deblurring via Multi-scale Separable Network

Wenqi Ren, Senyou Deng, Kaihao Zhang, Fenglong Song, Xiaochun Cao, Ming-Hsuan Yang

Abstract: Despite significant progress has been made in image and video deblurring, much less attention has been paid to process ultra high-definition (UHD) videos (e.g., 4K resolution). In this work, we propose a novel deep model for fast and accurate UHD video deblurring (UHDVD). The proposed UHDVD is achieved by a depth-wise separable-patch architecture, which operates with a multi-scale integration scheme to achieve a large receptive field without adding the number of generic convolutional layers and kernels. Additionally, we adopt the temporal feature attention module to effectively exploit the temporal correlation between video frames to obtain clearer recovered images. We design an asymmetrical encoder–decoder architecture with residual channel-spatial attention blocks to improve accuracy and reduce the depth of the network appropriately. Consequently, the proposed UHDVD achieves real-time performance on 4K videos at 30 fps. To train the proposed model, we build a new dataset comprised of 4K blurry videos and corresponding sharp frames using three different smartphones. Extensive experimental results show that our network performs favorably against the state-of-the-art methods on the proposed 4K dataset and existing 720p and 2K benchmarks in terms of accuracy, speed, and model size.