Semi-Blind Image Deblurring Based on Framelet Prior

TL;DR

This paper introduces a semi-blind image deblurring method that enhances the total variation approach using framelet transforms and fractional calculations, effectively restoring blurred images with approximate kernels.

Contribution

It proposes a novel semi-blind deblurring algorithm combining framelet transforms and fractional calculus to improve TV-based restoration.

Findings

Effective restoration of blurred images with approximate kernels.

Outperforms existing methods in various tests.

Applicable to different image types.

Abstract

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.