AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (9 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Blind image deblurring method based on l1/l2-norm regularization

Shengfang CAO Hongping HU ( )Wenke WANG
School of Mathematics, North University of China, Taiyuan 030051, China
Show Author Information

Abstract

Aiming at the problem of ringing artifacts existing in the edge of image in traditional blind image deblurring methods, l1/l2 regularization-based blind image deblurring method is proposed. The latent image is constrained by l1/l2 regularization, and the two-norm constraint is applied to the blur kernel to remove the noise of the blur kernel. During the solution process, the latent image and the blur kernel are updated alternately anditeratively, and the deblur redimage is finally obtained by combining the finest estimated blur kernel with the non-blind deblurring method. The experimental results show that the proposed method improves the quality of image deblurring and effectively removes some ringing artifacts. It has a good restoration effect on natural blurred images.

References

【1】
【1】
 
 
Journal of Measurement Science and Instrumentation
Pages 182-188

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
CAO S, HU H, WANG W. Blind image deblurring method based on l1/l2-norm regularization. Journal of Measurement Science and Instrumentation, 2023, 14(2): 182-188. https://doi.org/10.62756/jmsi.1674-8042.2023021

796

Views

43

Downloads

0

Crossref

0

CSCD

Received: 13 November 2022
Published: 01 June 2023
© The Author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.