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Proceedings Paper

Method of quality assessment based on convolution feature similarity for laser disturbing image
Author(s): Xiang Gao; Jing Hu; LiJun Ren; WeiPing Zheng; XiangJun Li
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Paper Abstract

At present, most of the full reference laser disturbing image quality assessment methods need to know the position information of the disturbing spot and the target in advance, so that the assessment process is restricted by the prior knowledge and the preprocessing method. Aiming at this problem, this paper proposes a laser disturbing image quality assessment method based on convolution feature similarity (CNNSIM), which analyzes the output features of the image before and after laser disturbing in the convolution network. The occlusion degree of key information in the disturbing image is assessed by using the hierarchy and the sensitivity to occlusion of features, thus avoiding the input requirement of target/spot location information. The simulation experiment verifies the effectiveness of the new assessment method in different scenarios.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301X (14 February 2020); doi: 10.1117/12.2541904
Show Author Affiliations
Xiang Gao, Huazhong Univ. of Science and Technology (China)
Jing Hu, National Key Lab. of Science and Technology on Multi-Spectral Information Processing (China)
LiJun Ren, Huazhong Univ. of Science and Technology (China)
WeiPing Zheng, Huazhong Univ. of Science and Technology (China)
XiangJun Li, Yan'an Univ. (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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