Fast motion deblurring using gyroscopes and strong edge prediction

This paper presents a fast deblurring algorithm to remove camera motion blur from a single photograph using built-in gyroscopes and strong edge prediction. An inaccurate blur kernel or point spread function (PSF) usually leads to an unsatisfying restored result. Hence, we propose a robust three-phase method for accurate PSF estimation. In the first stage, we utilize the embedded gyroscopes to compute a coarse version of the PSF from the camera's angular velocity during an exposure. In order to reduce the execution time of the later PSF modification, we introduce a patch selection procedure in the second stage to choose a suitable region from the blurry image based on the size of the coarse PSF estimated in stage one. The third phase aims to modify the coarse PSF to obtain an accurate one by predicting strong edges from an estimated latent image. In our experiments, we compare the restoration performance of several state-of-the-art approaches including ours and find that the proposed method outperforms others qualitatively as well as quantitatively. In addition, our method is also compared with the multi-scale approach without gyroscope data and shows shorter processing time and comparable deblurring quality. To the best of our knowledge, this is the first work that combines the sensor-aided method with the image-based approach to estimate the blur kernel.