摘要
红外偏振成像系统快速发展且应用广泛,但评估其性能的成像系统性能模型发展不足。迫切需要能够与先进的偏振成像系统相匹配的性能模型。利用深度学习网络的训练过程与人脑提取认知信息过程的相似性,本文首次将深度学习方法引入系统性能模型领域,提出了一种基于二维图像的可自动评估系统性能的红外偏振成像系统性能模型。该模型主要包含两个主要模块:退化模块、性能感知模块。在评估一个新的系统时,需要输入高质量的原始图像,并根据系统的硬件参数量身定制成像系统退化模块,退化完成后输入性能感知模块,从而得到最终的目标获取性能。为验证模型有效性,本文基于红外辐射理论自建了面向海面场景的红外偏振数据集,训练网络并进行测试。应用该模型对红外偏振成像系统的性能进行评估,评估结果与主观感知具有较好的一致性。
Although infrared polarization imaging systems have been developed rapidly and widely,a model for evaluating their performance has not been sufficiently developed.Performance models that can match advanced polarization imaging systems are urgently required.Regarding the similarity between the training process of a deep learning network and the process of extracting cognitive information from the human brain,this paper introduces a deep learning method in the field of system performance modeling for the first time and proposes a performance model for infrared polarization imaging systems that can automatically evaluate system performance based on two-dimensional images.The model includes two main modules:a degradation module and a performance awareness module.When evaluating a new system,high-quality original images are input and sequentially passed through an imaging system degradation module,customized according to the hardware parameters of the system,and input into a performance awareness module to obtain the final target acquisition performance.Moreover,to verify the effectiveness of the model,we realized a self-built infrared polarization dataset for sea surface scenes based on infrared radiation theory,and trained and tested the networks.The results obtained when the model was applied to evaluate the performance of infrared polarization imaging systems showed good agreement with subjective perception.
作者
王霞
赵家碧
孙琪扬
金伟其
WANG Xia;ZHAO Jiabi;SUN Qiyang;JIN Weiqi(School of Optoelectronics,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Optoelectronic Imaging Technology and System,Ministry of Education,Beijing Institute of Technology,Beijing 100081,China)
出处
《红外技术》
CSCD
北大核心
2023年第5期437-445,共9页
Infrared Technology
基金
国家自然科学基金资助项目(62171024)。
关键词
红外偏振成像
性能模型
深度学习
海面场景数据集
infrared polarization imaging
performance model
deep learning
sea scene dataset
