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Non-uniform Deblur Algorithm using Gyro Sensor and Different Exposure Image Pair
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  • Journal title : Journal of Broadcast Engineering
  • Volume 21, Issue 2,  2016, pp.200-209
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2016.21.2.200
 Title & Authors
Non-uniform Deblur Algorithm using Gyro Sensor and Different Exposure Image Pair
Ryu, Ho-hyeong; Song, Byung Cheol;
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 Abstract
This paper proposes a non-uniform de-blur algorithm using IMU sensor and a long/short exposure-time image pair to efficiently remove the blur phenomenon. Conventional blur kernel estimation algorithms using sensor information do not provide acceptable performance due to limitation of sensor performance. In order to overcome such a limitation, we present a kernel refinement step based on images having different exposure times which improves accuracy of the estimated kernel. Also, in order to figure out the phenomenon that conventional non-uniform de-blur algorithms suffer from severe degradation of visual quality in case of large blur kernels, this paper a homography-based residual de-convolution which can minimize quality degradation such as ringing artifacts during de-convolution. Experimental results show that the proposed algorithm is superior to the state-of-the-art methods in terms of subjective as well as objective visual quality.
 Keywords
Deblurring;non-uniform blur;IMU;gyro;
 Language
Korean
 Cited by
 References
1.
A. Levin, Y. Weiss, F. Durand, W. T. Freeman, "Understanding and Evaluating Blind De-convolution Algorithm," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1964-1971, Jun. 2009..

2.
L. Yuan, J. Sun, L. Quan, H. Y. Shum, “Image De-blurring with Blurred/noisy Image Pairs,” ACM Trans. Graphics, vol. 26, no. 3, pp. 1-10, Jul. 2007. crossref(new window)

3.
S. Cho, H. Cho, Y. W. Tai, S. Lee, "Registration based Non-uniform Motion Deblurring," in Proc., Comp. Graph. Forum, vol. 31, no. 7, pp. 2183-2192, Sep. 2012. crossref(new window)

4.
Y. W. Tai, P. Tan, S. Brown, “Richardson-Lucy Deblurring for Scenes under a Projective Motion Path,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1603-1618, Dec. 2011. crossref(new window)

5.
O. Whyte, J. Sivic, A. Zisserman, J. Ponce, “Non-uniform Deblurring for Shaken Images,” Int. J. Comput. Vis., vol. 98, no. 2, pp. 168-186, Jun. 2011. crossref(new window)

6.
L. Xu, J. Jia, "Two-phase Kernel Estimation for Robust Motion Deblurring," in Proc., Eur. Conf. Comput. Vis. (ECCV), vol. 6311, pp. 157-170, 2010.

7.
S. Baker, M. Iain, “Lucas-Kanade 20 Years on: A Unifying Framework,” Int. J. Comput. Vision, vol. 56, no. 3, pp. 221-255, Feb. 2004. crossref(new window)

8.
L. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, "BM3D: Image Denoising with Shape-adaptive Principal Component Analysis," Workshop on Signal Processing with Adaptive Sparse Structured Representations, Apr. 2009.

9.
D. Krishnan, R. Szeliski, "Fast Image Deconvolution using Hyper-Laplacian Prior," in Advances in Neural Information Processing Systems, pp. 1033-1041, 2009.

10.
J. Chen, L. Yuan, C. K. Tang, and L. Quan., "Robust dual motion deblurring," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Jun. 2008.

11.
N. Joshi, S.B. Kang, L. Zitnick, and R. Szeliski, “Image Deblurring using Inertial Measurement Sensors,” ACM Trans. Graphics, vol. 29, no. 30, Jul. 2010.

12.
A. Levin, R. Fergus, F. Durand, W. T. Freeman, “Image and Depth from a Conventional Camera with a Coded Aperture,” ACM Trans. Graphics, vol. 26, no. 3, Aug. 2007. crossref(new window)