Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-05T06:54:06.189Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  05 June 2014

By
Yu-Wing Tai  and
Michael S. Brown
Edited by
A. N. Rajagopalan  and
Rama Chellappa
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
Get access

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×