Performance Evaluation Model for Infrared Polarization Imaging System

WANG Xia; ZHAO Jiabi; SUN Qiyang; JIN Weiqi

Volume 45 Issue 5

May 2023

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WANG Xia, ZHAO Jiabi, SUN Qiyang, JIN Weiqi. Performance Evaluation Model for Infrared Polarization Imaging System[J]. Infrared Technology , 2023, 45(5): 437-445.

Citation:

WANG Xia, ZHAO Jiabi, SUN Qiyang, JIN Weiqi. Performance Evaluation Model for Infrared Polarization Imaging System[J]. Infrared Technology , 2023, 45(5): 437-445.

WANG Xia, ZHAO Jiabi, SUN Qiyang, JIN Weiqi. Performance Evaluation Model for Infrared Polarization Imaging System[J]. Infrared Technology , 2023, 45(5): 437-445.

Citation:

WANG Xia, ZHAO Jiabi, SUN Qiyang, JIN Weiqi. Performance Evaluation Model for Infrared Polarization Imaging System[J]. Infrared Technology , 2023, 45(5): 437-445.

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Performance Evaluation Model for Infrared Polarization Imaging System

WANG Xia^{1, 2
,},
ZHAO Jiabi^{1, 2},
SUN Qiyang^{1, 2},
JIN Weiqi^{1, 2}

1.
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
2.
Key Laboratory of Optoelectronic Imaging Technology and System, Ministry of Education, Beijing Institute of Technology, Beijing 100081, China

Received Date: 2023-04-14
Rev Recd Date: 2023-04-28
Publish Date: 2023-05-20

Abstract

Abstract

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.
- infrared polarization imaging,
- performance model,
- deep learning,
- sea scene dataset

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References(18)

References

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