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arXiv · 2211.11423

Blur Interpolation Transformer for Real-World Motion from Blur

Zhihang Zhong·Mingdeng Cao·Xiang Ji·Yinqiang Zheng·Imari Sato
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Abstract

This paper studies the challenging problem of recovering motion from blur, also known as joint deblurring and interpolation or blur temporal super-resolution. The challenges are twofold: 1) the current methods still leave considerable room for improvement in terms of visual quality even on the synthetic dataset, and 2) poor generalization to real-world data. To this end, we propose a blur interpolation transformer (BiT) to effectively unravel the underlying temporal correlation encoded in blur. Based on multi-scale residual Swin transformer blocks, we introduce dual-end temporal supervision and temporally symmetric ensembling strategies to generate effective features for time-varying motion rendering. In addition, we design a hybrid camera system to collect the first real-world dataset of one-to-many blur-sharp video pairs. Experimental results show that BiT has a significant gain over the state-of-the-art methods on the public dataset Adobe240. Besides, the proposed real-world dataset effectively helps the model generalize well to real blurry scenarios. Code and data are available at https://github.com/zzh-tech/BiT.

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Zhihang Zhong, Mingdeng Cao, Xiang Ji, Yinqiang Zheng, Imari Sato. 2022-11-21. Blur Interpolation Transformer for Real-World Motion from Blur. https://arxiv.org/abs/2211.11423

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