Semi-Blind Image Deblurring Based on Framelet Prior
M. Zarebnia R. Parvaz
The problem of image blurring is one of the most studied topics in the field
of image processing. Image blurring is caused by various factors such as hand
or camera shake. To restore the blurred image, it is necessary to know
information about the point spread function (PSF). And because in the most
cases it is not possible to accurately calculate the PSF, we are dealing with
an approximate kernel. In this paper, the semi-blind image deblurring problem
are studied. Due to the fact that the model of the deblurring problems is an
ill-conditioned problem, it is not possible to solve this problem directly. One
of the most efficient ways to solve this problem is to use the total variation
(TV) method. In the proposed algorithm, by using the framelet transform and
fractional calculations, the TV method is improved. The proposed method is used
on different types of images and is compared with existing methods with
different types of tests.