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The Analysis of Image Enhancement for Target Detection

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Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10942))

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Abstract

In the process of automatic detection and recognition based on image, the quality of the detected images affects the target detection and recognition results. To solve the problem of low contrast and high signal-to-noise ratio of the target image in the target detection process, this paper introduces two types of image detail enhancement algorithms which are widely used in recent years, including brightness contrast image enhancement algorithm and HSV color space based enhancement algorithm, and its impact on the target detection. Experiments show that the image detail enhancement can improve the overall and local contrast of the image, highlight the details of the image, and the enhanced image can effectively improve the number and accuracy of the target detection.

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References

  1. Hao, Z.C., Wu, C., Yang, H., Zhu, M.: Image detail enhancement method based on multi-scale bilateral texture filter. J. Chin. Opt. 9(4), 423–431 (2016)

    Article  Google Scholar 

  2. Zimmerman, J.B., Pizer, S.M., Staab, E.V., et al.: An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. J. IEEE Trans. Med. Imaging 7(4), 304–312 (1988)

    Article  Google Scholar 

  3. Wang, Q., Ward, R.: Fast image/video contrast enhancement based on WTHE. J. IEEE Trans. Consum. Electron. 53(2), 757–764 (2007)

    Article  Google Scholar 

  4. Yang, S., Oh, J.H., Park, Y.: Contrast enhancement using histogram equalization with bin underflow and bin overflow. In: Proceedings 2003 International Conference on Image Processing, Spain, pp. 881–884(2003)

    Google Scholar 

  5. Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. J. IEEE Trans. Cons. Electron. 43(1), 1–8 (1997)

    Article  Google Scholar 

  6. Kim, W.K., You, J.M., Jeong, J.: Contrast enhancement using histogram equalization based on logarithmic mapping. J. Opt. Eng. 51(6), 1–10 (2012)

    Article  Google Scholar 

  7. Fries, R., Modestino, J.: Image enhancement by stochastic homomorphic filtering. J. IEEE Trans. Acousties Speech Signal Process. 27(6), 625–637 (1979)

    Article  Google Scholar 

  8. Ein-Shoka, A.A., Kelash, H.M.: Enhancement of IR images using homomorphic filtering in fast discrete curvelet transform(FDCT). J. Int. J. Comput. Appl. 96(8), 22–25 (2014)

    Google Scholar 

  9. Delac, K., Grgic, M., Kos, T.: Sub-image homomorphic filtering technique for improving facial identification under difficult illumination conditions. In: International Conference on Systems, Signals and Image Processing, Budapest, Hungary, pp. 95–98 (2006)

    Google Scholar 

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Acknowledgments

This research work is supported by the grant of Guangxi science and technology development project (No: AC16380124), the grant of Guangxi Science Foundation (No: 2017GXNSFAA198226), the grant of Guangxi Key Laboratory of Trusted Software of Guilin University of Electronic Technology (No: KX201601), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics of Guilin University of Electronic Technology (No: GIIP201602), and the grant of Innovation Project of GUET Graduate Education (2017YJCX55), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics (No. GIIP201602),the grant of Guangxi Key Laboratory of Trusted Software (No. kx201601), Guangxi Cooperative Innovation Center of cloud computing and Big Data, the grant of Guangxi Colleges and Universities Key Laboratory of cloud computing and complex systems (No. YD16E11), the grant of Guangxi Key Laboratory of cryptography and information security (GCIS201601, GCIS201602, GCIS201603).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China

    Rui Zhang, Jinlong Chen & Xianjun Chen

  2. Beijing Institute of Special Electrometrical Technology, Beijing, 100190, China

    Yongjun Jia & Lihui Shi

  3. Key Laboratory of Intelligent Processing of Computer Image and Graphics, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China

    Hang Pan & Jinlong Chen

  4. Information Engineering School, Haikou College of Economics, Haikou, 571127, Hainan, China

    Xianjun Chen

Authors
  1. Rui Zhang
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  2. Yongjun Jia
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  3. Lihui Shi
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  4. Hang Pan
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  5. Jinlong Chen
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  6. Xianjun Chen
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Corresponding author

Correspondence to Xianjun Chen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Zhang, R., Jia, Y., Shi, L., Pan, H., Chen, J., Chen, X. (2018). The Analysis of Image Enhancement for Target Detection. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_46

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  • DOI: https://doi.org/10.1007/978-3-319-93818-9_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93817-2

  • Online ISBN: 978-3-319-93818-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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