论文标题
先前使用暂时清晰度的深度视频脱俗的视频
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior
论文作者
论文摘要
我们提出了一个简单有效的深度卷积神经网络（CNN）模型，用于视频脱张。所提出的算法主要包括来自中间潜在框架和潜在框架恢复步骤的光流估计。它首先开发了一个深CNN模型，以估算中间潜在帧的光流，然后根据估计的光流恢复潜在帧。为了更好地探索视频中的时间信息，我们在限制深CNN模型以帮助潜在的框架修复的情况下开发了一个时间清晰度。我们开发了一种有效的级联训练方法，并以端到端方式共同培训拟议的CNN模型。我们表明，探索视频DeBlurring的领域知识能够使Deep CNN模型更加紧凑和高效。广泛的实验结果表明，所提出的算法对基准数据集和现实世界视频的最先进方法表现出色。
We present a simple and effective deep convolutional neural network (CNN) model for video deblurring. The proposed algorithm mainly consists of optical flow estimation from intermediate latent frames and latent frame restoration steps. It first develops a deep CNN model to estimate optical flow from intermediate latent frames and then restores the latent frames based on the estimated optical flow. To better explore the temporal information from videos, we develop a temporal sharpness prior to constrain the deep CNN model to help the latent frame restoration. We develop an effective cascaded training approach and jointly train the proposed CNN model in an end-to-end manner. We show that exploring the domain knowledge of video deblurring is able to make the deep CNN model more compact and efficient. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods on the benchmark datasets as well as real-world videos.
