Cascaded Deep Video Deblurring Using Temporal Sharpness Prior

TL;DR

This paper introduces a deep CNN-based video deblurring method that utilizes optical flow estimation and a temporal sharpness prior, achieving improved performance on benchmark datasets and real-world videos.

Contribution

It proposes a novel cascaded training approach with a temporal sharpness prior, making the deep CNN model more compact and effective for video deblurring.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Efficient end-to-end training approach

Effective use of temporal sharpness prior

Abstract

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.