WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN

Author:

Li Yi1ORCID,Wang Nan1,Li Jinlong1,Zhang Yu1

Affiliation:

1. School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 611756, China

Abstract

Although the existing deblurring methods for defocused images are capable of approximately recovering clear images, they still exhibit certain limitations, such as ringing artifacts and remaining blur. Along these lines, in this work, a novel deep-learning-based method for image defocus deblurring was proposed, which can be applied to medical images, traffic monitoring, and other fields. The developed approach is equipped with wavelet transform, an iterative filter adaptive module, and graph neural network and was specifically designed for handling defocused blur. Our network exhibits excellent properties in preserving the original information during the restoration of clear images, thereby enhancing its capability to spatially address varying blurriness and improving the quality of deblurring. From the acquired experimental results, the superiority of the introduced method in the context of image defocus deblurring compared to the majority of the existing algorithms was clearly demonstrated.

Funder

Sichuan Province Science and Technology Support Program

National Natural Science Foundation of China

Guang‘an Science and Technology Innovation Project

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference24 articles.

1. Abuolaim, A., and Brown, M.S. (2020). Computer Vision—ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23–28 August 2020, Springer.

2. Ronneberger, O., Fischer, P., and Brox, T. (2015). Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Proceedings of the 18th International Conference, Munich, Germany, 5–9 October 2015, Springer.

3. Guo, T., Seyed Mousavi, H., Huu, V.T., and Monga, V. (2017, January 21–26). Deep wavelet prediction for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.

4. Lee, J., Son, H., Rim, J., Cho, S., and Lee, S. (2021, January 20–25). Iterative filter adaptive network for single image defocus deblurring. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.

5. Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3