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Proceedings Paper

Dim point target enhancement and detection based on improved NL-means in complex background
Author(s): Ping-Yue Lv; Chang-Qing Lin
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Paper Abstract

In order to achieve a single-frame enhancement and detection of dim point targets efficiently, a new method based on morphological top-hat transformation and modified non-local means (NL-means, NLM) for target enhancement is presented. After enhancing dim point targets, an estimating algorithm called local reverse entropy is applied to get candidate targets, and obtains detecting results in the end. In this model, white top-hat and black top-hat are combined to obtain pre-processed image, then the targets are enhanced for the second time through modified NL-means algorithm. The background in the residual image of enhanced image and original image is mostly suppressed, then it is regarded as the input of local reverse entropy estimation method (LREM). Detecting results can be obtained by setting proper thresholds. The 4 × 4 weak lattice targets with different brightness are superimposed on different infrared image backgrounds. The experimental results show that, when the SCR is low (SCR≈1), the detection algorithm model proposed in this paper has higher SCR gain than other target-enhancing algorithms such as TDLMS, max-median, max-mean, non-local means, etc. and the detection performance is the best.

Paper Details

Date Published: 21 July 2017
PDF: 11 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042017 (21 July 2017);
Show Author Affiliations
Ping-Yue Lv, Shanghai Institute of Technical Physics (China)
Univ. of Chinese Academy of Sciences (China)
Chang-Qing Lin, Shanghai Institute of Technical Physics (China)

Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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