Estimation of motion blur point spread function from differently exposed image frames

In this paper we investigate the problem of recovering the motion blur point spread function (PSF) by fusing the information available in two differently exposed image frames of the same scene. The proposed method exploits the difference between the degradations which affect the two images due to their different exposure times. One of the images is mainly affected by noise due to low exposure whereas the other one is mainly affected by motion blur caused by camera motion during the exposure time. Assuming certain models for the observed images and the blur PSF, we propose a maximum a posteriory (MAP) estimator of the motion blur. The experimental results show that the proposed method has the ability to estimate the motion blur PSF caused by rather complex motion trajectories, allowing a significant increase in the signal to noise ratio of the restored image.