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8 - Richardson–Lucy deblurring for scenes under a projective motion path

Published online by Cambridge University Press:  05 June 2014

Yu-Wing Tai
Affiliation:
Korea Advanced Institute of Science and Technology
Michael S. Brown
Affiliation:
National University of Singapore
A. N. Rajagopalan
Affiliation:
Indian Institute of Technology, Madras
Rama Chellappa
Affiliation:
University of Maryland, College Park
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Summary

Introduction

Motion blur from camera egomotion is an artifact in photography caused by the relative motion between the camera and an imaged scene during exposure. Assuming a static and distant scene, and ignoring the effects of defocus and lens aberration, each point in the blurred image can be described as the convolution of the un-blurred image by a point spread function (PSF) that describes the relative motion trajectory at that point's position. The aim of image deblurring is to reverse this convolution process to recover the clear image of the scene from the captured blurry image as shown in Figure 8.1.

A common assumption in existing motion deblurring algorithms is that the motion PSF is spatially invariant. This implies that all pixels are convolved with the same motion blur kernel. However, as discussed by Levin, Weiss, Durand & Freeman (2009) the global PSF assumption is often invalid. In their experiments, images taken with camera shake exhibited notable amounts of rotation that attributed to spatially-varying motion blur within the image. Figure 8.2 shows a photograph that illustrates this effect. As a result, Levin et al. (2009) advocated the need for a better motion blur model as well as image priors to help regularize the solution space when performing deblurring. This chapter addresses the former issue by introducing a new and compact motion blur model that is able to describe spatially-varying motion blur caused by a camera undergoing egomotion.

Type
Chapter
Information
Motion Deblurring
Algorithms and Systems
, pp. 161 - 183
Publisher: Cambridge University Press
Print publication year: 2014

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References

Agrawal, A. & Raskar, R. (2007). Resolving objects at higher resolution from a single motion-blurred image. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
Bardsley, J., Jefferies, S., Nagy, J. & Plemmons, R. (2006). Blind iterative restoration of images with spatially-varying blur. In Optics Express, pp. 1767–82.CrossRef
Ben-Ezra, M. & Nayar, S. (2003). Motion deblurring using hybrid imaging. In IEEE Conference on Computer Vision and Pattern Recognition, vol. I, pp. 657–64.
Ben-Ezra, M. & Nayar, S. (2004). Motion-based motion deblurring. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 689–98.CrossRefGoogle Scholar
Bini, D. A., Higham, N. J. & Meini, B. (2005). Algorithms for the matrix pth root. Numerical Algorithms, 39(4), 349–78.CrossRefGoogle Scholar
Chan, T. F. & Wong, C.-K. (1998). Total variation blind deconvolution. IEEE Transactions on Image Processing, 7(3), 370–5.CrossRefGoogle Scholar
Cho, S., Cho, H., Tai, Y.-W. & Lee, S. (2012). Registration based non-uniform motion deblurring. Computer Graphics Forum (Special Issue on Pacific Graphics), 31(7), 2183–92.Google Scholar
Cho, S. & Lee, S. (2009). Fast motion deblurring. ACM Transactions on Graphics, 28(5), 145:18.Google Scholar
Cho, S., Matsushita, Y. & Lee, S. (2007). Removing non-uniform motion blur from images. In IEEE International Conference on Computer Vision, pp. 1-8.
Cho, S., Wang, J. & Lee, S. (2011). Handling outliers innon-blind image deconvolution. In IEEE International Conference on Computer Vision, pp. 495–502.
Dai, S. & Wu, Y. (2008). Motion from blur. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
Dempster, A. D., Laird, N. M. & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39, 1-38.Google Scholar
Dey, N., Blanc-Fraud, L., Zimmer, C., Kam, Z., Roux, P., Olivo-Marin, J. & Zerubia, J. (2004). International Symposium on Biomedical Imaging: Nano to Macro, pp. 1223–6.
Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T. & Freeman, W. T. (2006). Removing camera shake from a single photograph. ACM Transactions on Graphics, 25(3), 787–94.CrossRefGoogle Scholar
Hirsch, M., Schuler, C., Harmeling, S. & Scholkopf, B. (2011). Fast removal of non-uniform camera shake. In IEEE International Conference on Computer Vision, pp. 463–70.
Hong, G. M., Rahmati, A., Wang, Y. & Zhong, L. (2008). Sensecoding: Accelerometer-assisted motion estimation for efficient video encoding. In Proceedings of the 16th ACM International Conference on Multimedia, pp. 749–52.CrossRef
Jia, J. (2007). Single image motion deblurring using transparency. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
Joshi, N., Kang, S. B., Zitnick, L. & Szeliski, R. (2010). Image deblurring with inertial measurement sensors. ACM Transactions on Graphics, 29(4), 30:1–9.Google Scholar
Krishnan, D. & Fergus, R. (2009). Fast image deconvolution using hyper-Laplacian priors. In Neural Information Processing Systems Conference, pp. 1033–41.
Levin, A. (2006). Blind motion deblurring using image statistics. In Neural Information Processing Systems Conference, pp. 841–8.
Levin, A., Fergus, R., Durand, F. & Freeman, W. T. (2007). Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics, 26(3), 70:1–9.Google Scholar
Levin, A., Weiss, Y., Durand, F. & Freeman, W. (2009). Understanding and evaluating blind deconvolution algorithms. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1964–71.CrossRef
Li, F., Yu, J. & Chai, J. (2008). A hybrid camera for motion deblurring and depth map super-resolution. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
Li, Y., Kang, S. B., Joshi, N., Seitz, S. & Huttenlocher, D. (2010). Generating sharp panoramas from motion-blurred videos. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424–31.
Lucy, L. (1974). An iterative technique for the rectification of observed distributions. The Astronomical Journal, 79, 745–54.CrossRefGoogle Scholar
Raskar, R., Agrawal, A. & Tumblin, J. (2006). Coded exposure photography: motion deblurring using fluttered shutter. ACM Transactions on Graphics, 25(3), 795–804.CrossRefGoogle Scholar
Richardson, W. (1972). Bayesian-based iterative method of image restoration. Journal of the Optical Society of America, 62(1), pp. 55–9.CrossRefGoogle Scholar
Sawchuk, A. A. (1974). Space-variant image restoration by coordinate transformations. Journal of the Optical Society of America, 64(2), 138–44.CrossRefGoogle Scholar
Shan, Q., Jia, J. & Agarwala, A. (2008). High-quality motion deblurring from a single image. ACM Transactions on Graphics, 27(3), 73:1–10.Google Scholar
Shan, Q., Xiong, W. & Jia, J. (2007). Rotational motion deblurring of a rigid object from a single image. In IEEE International Conference on Computer Vision, pp. 1-8.
Shepp, L. A. & Vardi, Y. (1982). Maximum likelihood reconstruction for emission tomography. IEEE Transactions on Medical Imaging, 1(2), 113–22.CrossRefGoogle Scholar
Tai, Y., Du, H., Brown, M. & Lin, S. (2008). Image/video deblurring using a hybrid camera. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
Tai, Y., Du, H., Brown, M. & Lin, S. (2010). Correction of spatially varying image and video blur using a hybrid camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6), 1012–28.CrossRefGoogle Scholar
Tai, Y., Kong, N., Lin, S. & Shin, S. (2010). Coded exposure imaging for projective motion deblurring. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2408–15.
Tai, Y.-W. & Lin, S. (2012). Motion-aware noise filtering for deblurring of noisy and blurry images. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 17-24.
Tai, Y.-W., Tan, P. & Brown, M. (2011). Richardson–Lucy deblurring for scenes under projective motion path. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1603–18.CrossRefGoogle Scholar
Vardi, Y. (1969). Nonlinear Programming. Englewood Cliffs, NJ: Prentice-Hall.
Whyte, O., Sivic, J., Zisserman, A. & Ponce, J. (2010). Non-uniform deblurring for shaken images. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 491–8. New York: Wiley.CrossRef
Xu, L. & Jia., J. (2010). Two-phase kernel estimation for robust motion deblurring. In European Conference on Computer Vision, pp. 157–70.
Yuan, L., Sun, J., Quan, L. & Shum, H.-Y. (2008). Progressive inter-scale and intra-scale nonblind image deconvolution. ACM Transactions on Graphics, 27(3), 74:1–10.Google Scholar

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