Motion blur kernel estimation using noisy inertial data

In the case of motion blur due to unknown motion, most of the existing image deblurring algorithms rely on good initial estimate of the kernel or latent image obtained through blind deconvolution and only consider 3-dimensional camera motions. To overcome these problems, Joshi [1] presented a novel blur kernel estimation and image deblurring approach by integrating 6-dimensional inertial sensors with a camera. However, the drift in the estimated camera motion path introduced by inertial measurement noise was not well handled in Joshi's work. In this paper, we propose an alternating optimization scheme to move the drifted camera motion path to the correct position. The camera pose space in the projective motion blur model is replaced by a motion path set to compensate for path drift. Experiments are performed on synthetic and real images to show the effectiveness of our approach.