Combining Motion Compensation with Spatiotemporal Constraint for Video Deblurring

We propose a video deblurring method by combining motion compensation with spatiotemporal constraint for restoring blurry video caused by camera shake. The proposed method makes effective full use of the spatiotemporal information not only in the blur kernel estimation, but also in the latent sharp frame restoration. Firstly, we estimate a motion vector between the current and the previous blurred frames, and introduce the estimated motion vector for deriving the motion-compensated frame with the previous restored frame. Secondly, we proposed a blur kernel estimation strategy by applying the derived motion-compensated frame to an improved regularization model for improving the quality of the estimated blur kernel and reducing the processing time. Thirdly, we propose a spatiotemporal constraint algorithm that can not only enhance temporal consistency, but also suppress noise and ringing artifacts of the deblurred video through introducing a temporal regularization term. Finally, we extend Fast Total Variation de-convolution (FTVd) for solving the minimization problem of the proposed spatiotemporal constraint energy function. Extensive experiments demonstrate that the proposed method achieve the state-of-the-art results either in subjective vision or objective evaluation.