Research on Cross-Correlative Blur Length Estimation Algorithm in Motion Blur Image

Abstract

This paper proposes a motion blur length estimation method that is applied to motion blur image restoration. This method applies a cross-correlation algorithm to multi-frame motion-degraded images. In order to find the motion blur parameters, the Radon transform method is used to estimate the motion blur angle. We extract the gray value of pixels around the blur center, calculate the correlation for obtaining motion blur length, and use the Lucy-Richardson iterative algorithm to restore the degraded image. Experiment results show that this method can accurately estimate blur parameters, reduce noise, and obtain better restoration results. The method achieves good results on artificially blurred images and natural images (by the camera shake). The advantage of our algorithm that uses the Lucy-Richardson restoration algorithm compared with the Wiener filtering algorithm is made obvious with less computation time and better restored effects.