Abstract
Video deblurring aims to restore sharp frames from blurry video sequences. Existing methods usually adopt optical flow to compensate misalignment between reference frame and each neighboring frame. However, inaccurate flow estimation caused by large displacements will lead to artifacts in the warped frames. In this work, we propose an equivalent receptive field deformable network (ERDN) to perform alignment at the feature level without estimating optical flow. The ERDN introduces a dual pyramid alignment module, in which a feature pyramid is constructed to align frames using deformable convolution in a cascaded manner. Specifically, we adopt dilated spatial pyramid blocks to predict offsets for deformable convolutions, so that the theoretical receptive field is equivalent for each feature pyramid layer. To restore the sharp frame, we propose a gradient guided fusion module, which incorporates structure priors into the restoration process. Experimental results demonstrate that the proposed method outperforms previous state-of-the-art methods on multiple benchmark datasets. The code is made available at: https://github.com/TencentCloud/ERDN.
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Jiang, B., Xie, Z., Xia, Z., Li, S., Liu, S. (2022). ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_38
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