Advertisement

Motion Blur Detection Using Convolutional Neural Network

  • R. B. PreethamEmail author
  • A. Thyagaraja Murthy
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

Abstract

In this paper, we identify movement obscure from a solitary, hazy picture. We propose a profound learning way to deal with and anticipate the likelihood dissemination of movement obscure at the fix level by utilizing a Convolutional Neural Network (CNN). The design we follow will moved toward the issue by cutting 100 pictures into 30 × 3 0 fixes and connected our movement obscure calculation to them (with an irregular rate of half). At that point named the hazy and non-foggy patches with 1 s (0 for still, 1 for hazy), and stacked the adjusted pictures as our preparation information. In this Paper, we aim to estimate blurred motion from a single blurry image and propose an in-depth learning approach to predict probabilistic patch level movement blur distribution using a Convolutional Neural Network (CNN).

Keywords

Convolutional Neural Network Motion deblur Python NumPy OpenCV 

References

  1. 1.
    Chakrabarti, A., Zickler, T., Freeman, W.: Analyzing spatially-varying blur. In: CVPR, pp. 2512–2519 (2010)Google Scholar
  2. 2.
    Couzinie-Devy, F., Sun, J., Alahari, K., Ponce, J.: Learning to estimate and remove the non-uniform image blur. In: Foster, I., Kesselman, C. (eds.) The Grid: Blueprint for a New Computing Infrastructure. CVPR, 2013. Morgan Kaufmann, San Francisco (1999)Google Scholar
  3. 3.
    Dai, S., Wu, Y.: Motion from blur. In: CVPR (2008)Google Scholar
  4. 4.
    Gupta, A., Joshi, N., Lawrence Zitnick, C., Cohen, M., Curless, B.: Single image deblurring using motion density functions. In: ECCV (2010)Google Scholar
  5. 5.
    Hirsch, M., Schuler, C., Harmeling, S., Scholkopf, B. Fast removal of non-uniform camera shake. In: ICCV (2011)Google Scholar
  6. 6.
    Ji, H., Wang, K.: A two-stage approach to blind spatially-varying motion deblurring. In: CVPR (2012)Google Scholar
  7. 7.
    Kim, T.H., Lee, K.M.: Segmentation-free dynamic scene deblurring. In: CVPR (2014)Google Scholar
  8. 8.
    Schmidt, U., Rother, C., Nowozin, S., Jancsary, J., Roth, S.: Discriminative non-blind deblurring. In: CVPR (2013)Google Scholar
  9. 9.
    Tai, Y., Tan, P., Brown, M.: Richardson-lucy deblurring for scenes under a projective motion path. IEEE T. PAMI 33(8), 1603–1618 (2011)CrossRefGoogle Scholar
  10. 10.
    Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. IJCV 98(2), 168–186 (2012)Google Scholar
  11. 11.
    Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: NIPS (2014)Google Scholar
  12. 12.
    Xu, L., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: CVPR (2013)Google Scholar
  13. 13.
    Zheng, S., Xu, L., Jia, J.: Forward motion deblurring. In: ICCV (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of E&CSri Jayachamarajendra College of Engineering, JSS Science and Technology UniversityMysuruIndia

Personalised recommendations