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Adaptive Morphological Contrast Enhancement Based on Quantum Genetic Algorithm for Point Target Detection

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Abstract

Robust point target detection of infrared clutter background has drawn great interest of scholars. Recently, morphological filter is playing a significant role in detecting infrared point target. Generally, the background clutter and targets are diverse in the case of each image. Traditional fixed structural elements and dimensions cannot be adjusted adaptively to acquire to successful point target detection in different complex backgrounds. Therefore, a new method is introduced based on quantum genetic algorithm to optimize and obtain structural element which is used as morphological filter for small target detection in original infrared images.Then,morphological contrast enhancement is further proposed to enhance energy of point targets after the filtered image is obtained.Thus, an enormous background clutter and noise are suppressed and the contrast between target and background are observably increased. Finally, by setting proper threshold, the point targets can be detected perfectly. Experimental evaluation results show that the proposed adaptive morphological contrast enhancement based on quantum genetic algorithm is effective and robust with respect to detection accuracy compared with the traditional morphological filter and other filtering algorithms.

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Acknowledgments

This work has been supported by the National Natural Science Foundation of China (under grant number of 61563049).

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Authors and Affiliations

  1. Institute of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Zhang Guofeng & Askar Hamdulla

  2. Changji Vocational and Technical College, Changji, 831100, China

    Zhang Guofeng

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  1. Zhang Guofeng
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  2. Askar Hamdulla
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Correspondence to Askar Hamdulla.

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Guofeng, Z., Hamdulla, A. Adaptive Morphological Contrast Enhancement Based on Quantum Genetic Algorithm for Point Target Detection. Mobile Netw Appl 26, 638–648 (2021). https://doi.org/10.1007/s11036-019-01410-8

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  • DOI: https://doi.org/10.1007/s11036-019-01410-8

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