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Blind Motion Deblurring for Online Defect Visual Inspection

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Book cover Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

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Abstract

Online defect visual inspection (ODVI) works while the object has to be static, otherwise the relative motion between camera and object will create motion blur in images. In order to implement ODVI in dynamic scene, it developes one blind motion deblurring method whose objective is to estimate blur kernel parameters precisely. In the proposed method, Radon transform on superpixels determinated the blur angle, and the autocorrelation function based on magnitude (AFM) of the preprocessed blurred image was utilized to identify the blur length. With the projection relationship discussed in this study, it will be unnecessary to rotate the blurred image or the axis. The proposed method is of high accuracy and robustness to noise, and it can somehow handle saturated pixels. To validate the proposed method, experiments have been carried out on synthetic images both in noise free and noisy situations. The results show that the method outperforms existing approaches. With the modified Richardson–Lucy deconvolution, it demonstrates that the proposed method is effective for ODVI in terms of subjective visual quality.

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Acknowledgments

Guangzhou Science and Technology Plan Project (201802030006), the Open Project Program of Guangdong Key Laboratory of Modern Geometry and Mechanics Metrology Technology (SCMKF201801).

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China

    Guixiong Liu & Bodi Wang

  2. School of Physics, South China University of Technology, Guangzhou, 510640, China

    Junfang Wu

Authors
  1. Guixiong Liu
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  2. Bodi Wang
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  3. Junfang Wu
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Corresponding author

Correspondence to Guixiong Liu .

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Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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Liu, G., Wang, B., Wu, J. (2019). Blind Motion Deblurring for Online Defect Visual Inspection. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_5

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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