Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-19T01:48:19.522Z Has data issue: false hasContentIssue false

3 - Hybrid-imaging for motion deblurring

Published online by Cambridge University Press:  05 June 2014

Moshe Ben-Ezra
Affiliation:
Massachusetts Institute of Technology
Yu-Wing Tai
Affiliation:
Korea Advanced Institute of Science and Technology
Michael S. Brown
Affiliation:
National University of Singapore
Shree K. Nayar
Affiliation:
Columbia University
A. N. Rajagopalan
Affiliation:
Indian Institute of Technology, Madras
Rama Chellappa
Affiliation:
University of Maryland, College Park
Get access

Summary

Introduction

This chapter introduces a hybrid-imaging system for motion deblurring, which is an imaging system that couples two or more cameras that function differently to perform a unified task. The cameras are usually selected to have different specialized functions. For example, a hybrid stereo camera presented by Sawhney et al. (Sawhney, Guo, Hanna, Kumar, Adkins & Zhou 2001) utilizes two cameras with different spatial resolutions to obtain high resolution stereo output.

In the context of this chapter, a hybrid-imaging system refers to a standard high resolution camera, which we call the primary detector with an auxiliary low resolution camera called the secondary detector. The secondary detector shares a common optical path with the primary detector, but operates at a significantly higher frame rate.

The primary detector produces a high resolution, high quality colour image but is susceptible to motion blur, whereas the secondary detector output is a sequence of low resolution, often monochromatic and noisy images of the same scene taken during the exposure time of the primary detector. An example of the primary and secondary detectors' output is shown in Figure 3.1.

The image sequence produced by the secondary detector is of little visual use. However, it contains information about the motion of the camera during the exposure, or more precisely, the motion flow field of the image during integration time. While the camera motion and the observed flow field are not identical (e.g. the observed flow field includes information about moving objects in the scene as well as their depth), the idea of hybrid-imaging motion deblurring is that given the image sequence from the secondary detector, it would be possible to compute the blur function (or the PSF) at every point of the high resolution image taken by the primary detector.

Type
Chapter
Information
Motion Deblurring
Algorithms and Systems
, pp. 57 - 74
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

Ben-Ezra, M. & Nayar, S. (2004). Motion-based Motion Deblurring. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 689–98.Google Scholar
Bioucas-Dias, J., Figueiredo, M. & Oliveira, J. (2006 a). Adaptive total-variation image deconvolution: A majorization-minimization approach. In Proceedings of the European Signal Processing Conference, pp. 1-4.
Bioucas-Dias, J., Figueiredo, M. & Oliveira, J. (2006 b). Total variation-based image deconvolution: a majorization-minimization approach. In IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 2, p. 11.
Chuang, Y., Curless, B., Salesin, D. H. & Szeliski, R. (2001). A Bayesian approach to digital matting. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–71.
Dey, N., Blanc-Feraud, L., Zimmer, C., Roux, P., Kam, Z., Olivo-Marin, J. & Zerubia, J. (2006). Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution, Microscopy Research and Technique, 69(4), 260–6.Google Scholar
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), pp. 787–94.Google Scholar
Jansson, P. A. (1997). Deconvolution of Image and Spectra, 2nd edn. Academic Press.
Joshi, N., Szeliski, R. & Kriegman, D. (2008). PSF estimation using sharp edge prediction. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
Krishnan, D. & Fergus, R. (2009). Fast image deconvolution using hyper-Laplacian priors. Advances in Neural Information Processing Systems, 22, 1-9.Google Scholar
Lauer, T. (2002). Deconvolution with a spatially-variant PSF. In SPIE Proceedings, Astronomical Data Analysis II, Vol. 4847, pp. 167–73.
Levin, A., Fergus, R., Durand, F. & Freeman, W. (2007). Deconvolution using natural image priors. Massachusetts Institute of Techmology, Computer Science and Artificial Intelligence Laboratory.
Lin, H., Tai, Y.-W. & Brown, M. S. (2011). Motion regularization for matting motion blurred objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), 2329–36.Google Scholar
Lucas, B. & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proceedings of the Defense Advanced Research Projects Agemy, pp. 121–30.
Sawhney, H., Guo, Y., Hanna, K., Kumar, R., Adkins, S. & Zhou, S. (2001). Hybrid stereo camera: an IBR approach for synthesis of very high resolution stereoscopic image sequences. In ACM Proceedings of the 28 th Annual Conference on Computer Graphics and Interactive Techniques, pp. 451–60.
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.
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.Google Scholar
Wiener, N. (1964). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. MIT Press.
Yuan, L., Sun, J., Quan, L. & Shum, H. (2007). Image deblurring with blurred/noisy image pairs. ACM Transactions on Graphics, 26(3), 1:1–10.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org 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
×