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

One of the most important issues in 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 problem 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, a fraction gradient operator is used instead of the ordinary gradient operator. The proposed method is investigated on different kinds of images such as text, face and natural. The output of the proposed method reflects the effectiveness of the proposed algorithm in restoring images from blind problems.