  The image blurring process is generally modelled as the convolution of a blur
kernel with a latent image. Therefore, the estimation of the blur kernel is
essentially important for blind image deblurring. Unlike existing approaches
which focus on approaching the problem by enforcing various priors on the blur
kernel and the latent image, we are aiming at obtaining a high quality blur
kernel directly by studying the problem in the frequency domain. We show that
the auto-correlation of the absolute phase-only image can provide faithful
information about the motion (e.g. the motion direction and magnitude, we call
it the motion pattern in this paper.) that caused the blur, leading to a new
and efficient blur kernel estimation approach. The blur kernel is then refined
and the sharp image is estimated by solving an optimization problem by
enforcing a regularization on the blur kernel and the latent image. We further
extend our approach to handle non-uniform blur, which involves spatially
varying blur kernels. Our approach is evaluated extensively on synthetic and
real data and shows good results compared to the state-of-the-art deblurring
approaches.
