Review of Motion Blur Estimation Techniques

The goal of image restoration is to improve a given image in some predefined sense. Restoration attempts to recover an image by modelling the degradation function and applying the inverse process. Motion blur is a common type of degradation which is caused by the relative motion between an object and camera. Motion blur can be modeled by a point spread function consists of two parameters angle and length. Accurate estimation of these parameters is required in case of blind restoration of motion blurred images. This paper compares different approaches to estimate the parameters of a motion blur namely direction and length directly from the observed image with and without the influence of Gaussian noise. These estimated motion blur parameters can then be used in a standard nonblind deconvolution algorithm. Simulation results compare the performance of most common motion blur estimation methods.