Point spread function estimation for blind image deblurring problems based on framelet transform

TL;DR

This paper introduces a novel algorithm for blind image deblurring that estimates the point spread function using framelet transforms and a coarse-to-fine iterative approach, improving kernel and image restoration quality.

Contribution

It presents a new blind deblurring method combining framelet transform, $l_0-eta l_1$ regularization, and a fractional gradient operator, advancing kernel estimation techniques.

Findings

Effective kernel estimation across various image types

Improved image restoration quality demonstrated

Robustness to different blurring scenarios

Abstract

One of the most important issues in the image processing is the approximation of the image that has been lost due to the blurring process. These types of matters are divided into non-blind and blind problems. The second type of problem is more complex in terms of calculations than the first problems due to the unknown of original image and point spread function estimation. In the present paper, an algorithm based on coarse-to-fine iterative by $l_0-\alpha l_1$ regularization and framelet transform is introduced to approximate the spread function estimation. Framelet transfer improves the restored kernel due to the decomposition of the kernel to different frequencies. Also in the proposed model fraction gradient operator is used instead of ordinary gradient operator. The proposed method is investigated on different kinds of images such as text, face, natural. The output of the proposed method reflects the effectiveness of the proposed algorithm in restoring the images from blind problems.