Ground-to-air aircraft infrared image deblurring based on imaging degradation simulation

Author:

Qin KeORCID,Li Menghao,Feng Huajun,Yang Haibo1,Chen Jiaxin1ORCID,Chen YuetingORCID

Affiliation:

1. National Key Laboratory of Electromagnetic Space Security

Abstract

The issue of infrared image deblurring has been a significant concern. However, in some specific scenes, the current mainstream deblurring algorithms based on optimization or deep learning fail to provide satisfactory results. Aiming to address the ineffectiveness of deep learning methods due to the low-cost datasets' unavailability for specific scenes, we innovatively propose a relatively simple full-chain imaging degradation simulation method using ground-to-air aircraft infrared imaging scene as an example, which considers the effects of blur and noise caused by the atmosphere, imaging system, target motion and detector. Through this method, we could generate abundant blur-clear image pairs by altering various parameters. To enhance the neural network’s generalization ability and the deblurring performance in the specific scenes, we employ a two-step approach: pretraining on the public GoPro dataset and subsequent finetuning on the simulation dataset. After testing on the simulation dataset and some real-world images, we have discovered the importance of selecting a pretraining dataset that closely matches the scene degradation mode. Additionally, regardless of whether the model is pre-trained on the UIRD or GoPro dataset, there are significant enhancements in the deblurring effect following finetuning with our constructed simulation dataset. In summary, compared to the traditional deconvolution methods and the methods trained on a general dataset, our approach not only exhibits superior deblurring capabilities but also effectively mitigates noise and prevents the occurrence of artifactual textures such as ringing artifact.

Funder

National Natural Science Foundation of China

Publisher

Optica Publishing Group

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