Abstract
With the development of infrared technology, infrared small targets detection has attracted great interest of researchers. Top-hat filter is one of widely used methods for detecting infrared small target, and the structure elements have great influence on the performance of detection. The structure elements are desired to be adjusted adaptively. To this end, an adaptive structure elements optimization method based on quantum genetic algorithm (QGA) is introduced, and the convergence of QGA reveals the effectiveness of QGA. Experimental results show that the proposed adaptive top-hat filter based on QGA can achieve more stable infrared small target detection performance compared with the traditional top-hat filter.
Similar content being viewed by others
References
Bae T (2011) Small target detection using bilateral filter and temporal cross product in infrared images. Infrared Phys Technol 54:403–411
Bai X, Zhou F (2010) Analysis of new top-hat transformation and the application for infrared dim small target detection. Pattern Recogn 43(6):2145–2156
Caefer CE, Silverman J, Mooney JM, Salvo SD, Taylor RW (1998) Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters. Proc SPIE 3373:111–122
Chen CLP, Li H, Wei Y, Xia T, Yan Tang Y (2014) A local contrast method for small infrared target detection. IEEE Trans Geosci Remote Sens 52(1):574–581
Deng L, Zhu H (2015) Moving point target detection based on clutter suppression using spatial temporal local increment coding. Electron Lett 51(8):625–626
Deng H, Sun X, Liu M, Ye C (2016) Infrared small-target detection using multiscale gray difference weighted image entropy. IEEE Trans Aerosp Electron Syst 52(1):60–72
Deshpande S, Er M, Ronda V, Chan P (1999) Max-mean and max-median filters for detection of small-targets. Proc SPIE 3809:74–83
Dong W, Zhang J, Yang D, Liu D (2011) Homogeneous background prediction algorithm for detection of point target. Infrared Phys Technol 54(2):70–74
Gao C, Sang N, Huang R (2014) Biologically inspired scene context for object detection using a single instance. PLoS One 9(5):e98477
Han KH, Kim JH (2000) Genetic quantum algorithm and its application to combinatorial optimization problem. Proc Evol Comput 2:1354–1360
Harvey NR, Marshall S (1994) Using genetic algorithms in the design of morphological filters. In: Mathematical Morphology and Its Applications to Image Processing, pp 53–59
Hilliard CI (2000) Selection of a clutter rejection algorithm for real-time target detection from an airborne platform. Proc SPIE :74–84
Laboudi Z, Chikhi S (2012) Comparison of genetic algorithm and quantum genetic algorithm. Proc Int Arab J Inf Technol 9(3):243–250
Li Y, Lu H, Zhang L, Zhu J, Yang S, Hu X (2012) An automatic image segmentation algorithm based onweighting fuzzy c-means clustering. Soft Computing in Information Communication Technology 1:27–32
Li Y, Liang S, Bai B, Feng D (2014) Detecting and tracking dim small targets in infrared image sequences under complex backgrounds. Multimed Tools Appl 71(3):1179–1199
Li Y, Zhang Y, Yu J, Tan Y, Tian J, Ma J (2016a) A novel spatio-temporal saliency approach for robust DIM moving target detection from airborne infrared image sequences. Inf Sci 369:548–563
Li Y, Tao C, Tan Y, Shang K, Tian J (2016b) Unsupervised multilayer feature learning for satellite image scene classification. IEEE Geosci Remote Sens Lett 13(2):157–161
Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016c) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77
Liu G, Wang F, Liu Z (2016) Infrared aerial small target detection based on digital image processing. Multimedia Tools Appl 1–15. doi:10.1007/s11042-016-3568-y
Lu H, Zhang L, Zhang M, Hu X, Serikawa S (2010) A method for infrared image segment based on sharpfrequency localized contourlet transform and morphology. Proc Int Conf Intelligent Control Inf Process, PART 2 :79–82. doi:10.1109/ICICIP.2010.5564346
Lu H, Li Y, Zhang L, Yang S, Serikawa S (2012) Fast level set segmentation method in medical multisensor images detection. Int J Adv Comput Technol 4(23):475–482
Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and Computation 29(6):e3927
Qi S, Ma J, Tao C, Yang C, Tian J (2013) A robust directional saliency-based method for infrared small-target detection under various complex backgrounds. IEEE Geosci Remote Sens Lett 10(3):495–499
Serra J (1982) Image analysis and mathematical morphology. Academic Press, New York
Shor P (1994) Algorithms for quantum computation: discrete logarithms and factoring. In: Proc. Annual Symposium on the Foundation of Computer Sciences, pp 20–22
Yang L, Yang J, Yang K (2004) Adaptive detection for infrared small target under sea-sky complex background. Electron Lett 40(17):1083–1085
Zeng M, Li J, Peng Z (2006) The design of top-hat morphological filter and application to infrared target detection. Infrared Phys Technol 48(1):67–76
Zhu H, Zhang T, Deng L (2013) Indirect target detection method in FLIR image sequences. Infrared Phys Technol 60:15–23
Acknowledgements
This work is sponsored by the National Natural Science Foundation (Grant No. 61501259, 61401228), sponsored by China Postdoctoral Science Foundation (Grant No. 2016 M591891, 2015 M581841), sponsored by Postdoctoral Science Foundation of Jiangsu Province(Grant No.1501019A), sponsored by Natural Science Foundation of Jiangsu Province (Grant No. BK20140874, BK20150864), and sponsored by NUPTSF (Grant No. NY214041, NY215136, NY214145).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Deng, L., Zhu, H., Zhou, Q. et al. Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection. Multimed Tools Appl 77, 10539–10551 (2018). https://doi.org/10.1007/s11042-017-4592-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-017-4592-2