Abstract
We show that very fast deblurring can be achieved with simple machine learning. The most difficult step in deblurring is the estimation of the blur kernel. We show that we can estimate the blur kernel by recognizing the object.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kundur, D., Hatzinakos, D.: Blind image deconvolution. Sig. Process. Mag. IEEE 13(3), 43–64 (1996)
Chan, T., Wong, C.-K.: Total variation blind deconvolution. IEEE Trans. Image Process. 7, 370–375 (1998)
Liu, R., Jia, J.: Reducing boundary artifacts in image deconvolution. In: ICIP (2008)
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27, 73 (2008)
Wang, C., Sun, L., Chen, Z., Zhang, J., Yang, S.: Multiscale blind motion deblurring using local minimum. Inverse Prob. 26(1), 015003 (2010)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding blind deconvolution algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2354–2367 (2011)
Xu, L., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: CVPR (2013)
Perrone, D., Favaro, P.: Total variation blind deconvolution: the devil is in the details. Technical report - University of Bern (2014)
Schuler, C.J., et al.: Learning to deblur. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1439–1451 (2015)
Sun, J., et al.: Learning a convolutional neural network for non-uniform motion blur removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc.: Ser. B (Methodol.) 58(1), 267–288 (1996)
Drucker, H., et al.: Support vector regression machines. In: Advances in Neural Information Processing Systems (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Science Press
About this paper
Cite this paper
Takeshi, N. (2020). Fast Blind Deconvolution with Simple Machine Learning. In: Duan , B., Umeda, K., Hwang, W. (eds) Proceedings of the Seventh Asia International Symposium on Mechatronics. Lecture Notes in Electrical Engineering, vol 589. Springer, Singapore. https://doi.org/10.1007/978-981-32-9441-7_99
Download citation
DOI: https://doi.org/10.1007/978-981-32-9441-7_99
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9440-0
Online ISBN: 978-981-32-9441-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)