  Blind image restoration is a non-convex problem which involves restoration of
images from an unknown blur kernel. The factors affecting the performance of
this restoration are how much prior information about an image and a blur
kernel are provided and what algorithm is used to perform the restoration task.
Prior information on images is often employed to restore the sharpness of the
edges of an image. By contrast, no consensus is still present regarding what
prior information to use in restoring from a blur kernel due to complex image
blurring processes. In this paper, we propose modelling of a blur kernel as a
sparse linear combinations of basic 2-D patterns. Our approach has a
competitive edge over the existing blur kernel modelling methods because our
method has the flexibility to customize the dictionary design, which makes it
well-adaptive to a variety of applications. As a demonstration, we construct a
dictionary formed by basic patterns derived from the Kronecker product of
Gaussian sequences. We also compare our results with those derived by other
state-of-the-art methods, in terms of peak signal to noise ratio (PSNR).
