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Proceedings Paper

Detection research on low light level target with joint transform correlator
Author(s): Su Zhang; Jiyang Shang; Chi Chen; Wensheng Wang
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Paper Abstract

Low light level target detection has received more attentions in varieties of domains in recent years. In this paper we use hybrid optoelectronic joint transform correlator(HOJTC) for detecting and recognizing low light level target. It is thought to be one of the most effective methods in target detection. But because of the cluttered backgrounds and strong noises of the low light level target, it always can not be detected successfully. In order to solve this problem efficiently, firstly we choose sym4 wavelet function to achieve the purpose of wavelet de-noising. After that edge extraction processing is used to distinguish the useful target from the cluttered backgrounds with Sobel operator. At last processed targets can be put into HOJTC to obtain a pair of correlation peaks clearly. To prove this method, many experiments of low light level targets have been implemented with computer simulation method and optical experiment method. As an example a low light level image "deer" is presented. The results show that the low light level target can be detected from the cluttered backgrounds and strong noises with wavelet de-noising and Sobel operator successfully.

Paper Details

Date Published: 18 August 2011
PDF: 8 pages
Proc. SPIE 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, 819416 (18 August 2011); doi: 10.1117/12.899886
Show Author Affiliations
Su Zhang, Changchun Univ. of Science and Technology (China)
Jiyang Shang, Changchun Univ. of Science and Technology (China)
Chi Chen, Changchun Univ. of Science and Technology (China)
Wensheng Wang, Changchun Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8194:
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications
Makoto Ikeda; Nanjian Wu; Guangjun Zhang; Kecong Ai, Editor(s)

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