  Blind Image deblurring tries to estimate blurriness and a latent image out of
a blurred image. This estimation, as being an ill-posed problem, requires
imposing restrictions on the latent image or a blur kernel that represents
blurriness. Different from recent studies that impose some priors on the latent
image, this paper regulates the structure of the blur kernel. We propose a
kernel mixture structure while using the Gaussian kernel as a base kernel. By
combining multiple Gaussian kernels structurally enhanced in terms of scales
and centers, the kernel mixture becomes capable of modeling nearly
non-parametric shape of blurriness. A data-driven decision for the number of
base kernels to combine makes the structure even more flexible. We apply this
approach to a remote sensing problem to recover images from blurry images of
satellite. This case study shows the superiority of the proposed method
regulating the blur kernel in comparison with state-of-the-art methods that
regulates the latent image.
