VDTR: Video Deblurring with Transformer

• URL: http://arxiv.org/abs/2204.08023v1
• Date: Sun, 17 Apr 2022 14:22:14 GMT
• Title: VDTR: Video Deblurring with Transformer
• Authors: Mingdeng Cao, Yanbo Fan, Yong Zhang, Jue Wang, Yujiu Yang
• Abstract summary: Videoblurring is still an unsolved problem due to the challenging-temporal modeling process. This paper presents VDTR, an effective Transformer-based model that makes the first attempt to adapt for Transformer video dering.
• Score: 24.20183395758706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video deblurring is still an unsolved problem due to the challenging spatio-temporal modeling process. While existing convolutional neural network-based methods show a limited capacity for effective spatial and temporal modeling for video deblurring. This paper presents VDTR, an effective Transformer-based model that makes the first attempt to adapt Transformer for video deblurring. VDTR exploits the superior long-range and relation modeling capabilities of Transformer for both spatial and temporal modeling. However, it is challenging to design an appropriate Transformer-based model for video deblurring due to the complicated non-uniform blurs, misalignment across multiple frames and the high computational costs for high-resolution spatial modeling. To address these problems, VDTR advocates performing attention within non-overlapping windows and exploiting the hierarchical structure for long-range dependencies modeling. For frame-level spatial modeling, we propose an encoder-decoder Transformer that utilizes multi-scale features for deblurring. For multi-frame temporal modeling, we adapt Transformer to fuse multiple spatial features efficiently. Compared with CNN-based methods, the proposed method achieves highly competitive results on both synthetic and real-world video deblurring benchmarks, including DVD, GOPRO, REDS and BSD. We hope such a Transformer-based architecture can serve as a powerful alternative baseline for video deblurring and other video restoration tasks. The source code will be available at \url{https://github.com/ljzycmd/VDTR}.

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