用伪逆建模提升视频去模糊深度模型性能
VDPI: Video Deblurring with Pseudo-inverse Modeling
- 用深度网络拟合模糊过程并估计其伪逆
- 在多个数据集上显著提升去模糊效果
- 适合需要高泛化能力的视频修复场景
视频去模糊旨在从模糊且含噪的观测中恢复清晰序列。图像形成模型在传统基于模型的方法中起关键作用，限制了可能解的范围。然而，这仅适用于部分基于深度学习的方法。尽管深度学习模型已取得更优结果，传统基于模型的方法仍因灵活性而广泛应用。越来越多研究尝试结合两者以提升去模糊性能。本文提出通过使用模糊的伪逆，将图像形成模型知识引入深度学习网络。利用深度网络拟合模糊过程并估计伪逆，再与变分深度学习网络结合进行视频去模糊。实验表明，此类改进可显著提升深度学习模型在视频去模糊上的表现。此外，在不同数据集上的实验均取得显著性能提升，证明所提方法具备良好泛化性，适用于多种场景和相机。
原文摘要 · Abstract (English)
Video deblurring is a challenging task that aims to recover sharp sequences from blur and noisy observations. The image-formation model plays a crucial role in traditional model-based methods, constraining the possible solutions. However, this is only the case for some deep learning-based methods. Despite deep-learning models achieving better results, traditional model-based methods remain widely popular due to their flexibility. An increasing number of scholars combine the two to achieve better deblurring performance. This paper proposes introducing knowledge of the image-formation model into a deep learning network by using the pseudo-inverse of the blur. We use a deep network to fit the blurring and estimate pseudo-inverse. Then, we use this estimation, combined with a variational deep-learning network, to deblur the video sequence. Notably, our experimental results demonstrate that such modifications can significantly improve the performance of deep learning models for video deblurring. Furthermore, our experiments on different datasets achieved notable performance improvements, proving that our proposed method can generalize to different scenarios and cameras.
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