Video deblurring is to restore sharp frames from a blurry sequence. It is a challenging low-level vision task because the blur caused by camera shake, object motions and depth variations is heterogeneous in both spatial and temporal dimensions. Traditional methods usually work on a fixed spatiotemporal scale. However, the spatiotemporal scale of blurs in the video can vastly vary in the real-world situation. To address this challenge, we propose a Spatiotemporal Pyramid Network (SPN) to dynamically learn different spatiotemporal cues for video deblurring. Specifically, inside SPN, a spatiotemporal pyramid module is employed to effectively capture both spatial information and temporal information from the blurry sequence in a pyramid mode. An image reconstruction module constructs the sharp center frame through the obtained spatiotemporal information. Additionally, inspired by the statistical image prior and adversarial learning, we extend SPN and propose a Spatiotemporal Pyramid Generative Adversarial Network (SPGAN), which conducts adversarial discrimination in the gradient space. It helps the network produce more realistic sharp video frames. Experiments conducted on benchmarks demonstrate that the proposed methods achieve state-of-the-art results in terms of PSNR, SSIM and visual quality.

视频去模糊是为了从模糊序列中恢复清晰的帧。这是一项具有挑战性的低级视觉任务，因为由相机抖动，物体运动和深度变化引起的模糊在空间和时间维度上都是异质的。传统方法通常在固定的时空范围内工作。但是，在实际情况下，视频中模糊的时空范围可能会发生很大变化。为了解决这一挑战，我们提出了一个时空金字塔网络（SPN），以动态学习用于视频去模糊的不同时空线索。具体来说，在SPN内部，采用时空金字塔模块以金字塔模式从模糊序列中有效捕获空间信息和时间信息。图像重建模块通过获取的时空信息来构建清晰的中心帧。此外，受统计图像先验和对抗学习的启发，我们扩展了SPN并提出了时空金字塔生成对抗网络（SPGAN），该网络在梯度空间中进行对抗歧视。它有助于网络产生更逼真的清晰视频帧。在基准上进行的实验表明，所提出的方法在PSNR，SSIM和视觉质量方面均达到了最新水平。它有助于网络产生更逼真的清晰视频帧。在基准上进行的实验表明，所提出的方法在PSNR，SSIM和视觉质量方面均达到了最新水平。它有助于网络产生更逼真的清晰视频帧。在基准上进行的实验表明，所提出的方法在PSNR，SSIM和视觉质量方面均达到了最新水平。