Dim point target enhancement and detection based on improved NL-means in complex background

Ping-Yue Lv; Chang-Qing Lin

doi:10.1117/12.2281913

21 July 2017 Dim point target enhancement and detection based on improved NL-means in complex background

Ping-Yue Lv, Chang-Qing Lin

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042017 (2017) https://doi.org/10.1117/12.2281913
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

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Ping-Yue Lv and Chang-Qing Lin "Dim point target enhancement and detection based on improved NL-means in complex background", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042017 (21 July 2017); https://doi.org/10.1117/12.2281913

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KEYWORDS

Target detection

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