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Objective detection of shear distortion of low-light-level image intensifier based on global scanning and image patch edge feature analysis

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Abstract

Shear distortion is the defect brought in the manufacturing stage of optical fiber panel of low-light-level (LLL) image intensifier. The traditional detection method of such defects is purely based on visual observation, so the recording measure is rough and the amount of manual intervention is large. According to the above facts, an objective detection method of shear distortion of LLL image intensifier based on global scanning and image patch edge feature analysis is proposed. Firstly, the inclination of parallel lines is calculated to realize the normalized rotation of the target image; Then, the effective area is scanned globally by means of spatial kernel for local defect detection. The image in the kernel is processed to retain only the edge features, and then the proposed shear distortion detection strategy is applied to each edge in the processed image. Finally, the distortion points in the local image are restored to the target image through the image patch spatial coordinates. To substantiate the performance of the proposed method, a series of image tubes with diverse degrees of shear distortion are put into experiments, and the relevant detection technologies are used as the comparison. It yields the conclusion that the proposed method is robust to the background noise, illumination change and image defects to some extent, and is superior to the relevant detection technology in overall performance. Compared with the traditional visual inspection method, this method not only standardizes the recording measure of test results, but also has better time stability.

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References

  1. Ai KC (2006) The progress and development of night vision technology. J Appl Opt 27(4):303–307. (In Chinese)

    Google Scholar 

  2. Bosch LA (2000) Image intensifier tube performance is what matters, image intensifiers and applications II. International Society for Optics and Photonics

  3. Canny JF (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  4. Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access PP(99):1–1

    Google Scholar 

  5. GJB 2000-94 (1994) General specification for image intensifier assembly. (in Chinese)

  6. Gonzalez RC, Woods RE (2010) Digital image processing. Publishing House of Electronics Industry

  7. He B, Ma TY (2004) Visual C++ digital image. Post & Telecom Press, Beijing. (In Chinese)

    Google Scholar 

  8. Marinica M, Liviu C, Mihai J, Gheorghe C (1998) Real performance of image intensifier systems for night vision. Proc SPIE 3405:926–929

    Article  Google Scholar 

  9. Oppenheim AV (1996) Signals and systems, 2nd edn. Pearson Education, London

    Google Scholar 

  10. Palm WJ (2008) Concise introduction to Matlab, Business & Economics

  11. Xiang SM, Ni GQ (1999) The principle of photo-electronic imaging devices. National Defense Industry Press, pp 368–377. (In Chinese)

  12. Wang Z, Wu F, Hu Z (2009) MSLD: a robust descriptor for line matching. Pattern Recogn 42(5):941–953

    Article  Google Scholar 

  13. Wang LZ, Tan S, Qian YS, Zhu SC (2021) Automatic evaluation of vertex structural defects on the anode surface of a low-light-level image intensifier based on proposed individual image processing strategies. Appl Opt 60:6888–6901

    Article  Google Scholar 

  14. WJ 2091-92 (1992) Test method of low light level image intensifier. (in Chinese)

  15. Zhang Y, Chu J, Leng L, Miao J (2020) Mask refined R-CNN: a network for refining object details in instance segmentation. Sensors 20(4):1010

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Information Engineering, Nanyang Institute of Technology, Nanyang, Henan, 473004, China

    Luzi Wang & Zhuanghui Li

  2. North Night Vision Technology Co., Ltd, Kunming, Yunnan, 650114, China

    Fengge Liu

Authors
  1. Luzi Wang
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  2. Zhuanghui Li
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  3. Fengge Liu
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Contributions

Conceptualization, L.W.; methodology, L.W.; software, L.W.; validation, L.W. and Z.L.; formal analysis, Z.L.; investigation, F.L.; resources, L.W.; data curation, F.L.; writing—original draft preparation, L.W.; writing—review and editing, Z.L.; visualization, F.L.; supervision, Z.L.; project administration, F.L.. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Luzi Wang.

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Wang, L., Li, Z. & Liu, F. Objective detection of shear distortion of low-light-level image intensifier based on global scanning and image patch edge feature analysis. Multimed Tools Appl 83, 3451–3471 (2024). https://doi.org/10.1007/s11042-023-15269-1

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  • DOI: https://doi.org/10.1007/s11042-023-15269-1

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