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14 February 2020 Method of quality assessment based on convolution feature similarity for laser disturbing image
Xiang Gao, Jing Hu, LiJun Ren, WeiPing Zheng, XiangJun Li
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Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301X (2020) https://doi.org/10.1117/12.2541904
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
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.
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Xiang Gao, Jing Hu, LiJun Ren, WeiPing Zheng, and XiangJun Li "Method of quality assessment based on convolution feature similarity for laser disturbing image", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301X (14 February 2020); https://doi.org/10.1117/12.2541904
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KEYWORDS
Image quality
Convolution
Detection and tracking algorithms
Image visualization
Optical simulations
Visualization
Deconvolution
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