Combining weighted curvelet accumulation with motion vector duty cycle for nonuniform video deblurring

Since object motion and camera shake can cause motion blur, there are some blurry frames in the captured videos by hand-held camera. In order to solve the above problem, we propose a nonuniform video deblurring method that combines weighted curvelet accumulation with motion vector duty cycle. In the proposed method, firstly we propose a weighted curvelet accumulation method that can synthesize the multiple adjacent frames in the frequency domain for estimating the initial latent sharp frame. Secondly, because the duty cycle has a close relation with the accuracy of the blur kernel, we propose a motion vector duty cycle estimation method by utilizing the inter-frame correlation information and the estimated initial latent sharp frame to improve the blur kernel accuracy. Finally, we build a novel nonblind video deblurring model by fully utilizing the spatiotemporal information and the estimated blur kernel for obtaining the deblurred frame. Experimental results on the numerous videos show that the proposed method achieves the state-of-the-art results either in subjective vision or in objective evaluation.