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Video enhancement using reference photographs

Published: 05 August 2007 Publication History

Abstract

Handheld digital video cameras have become increasingly popular and cheaper in recent years. Even still cameras offer additional functionality for shooting videos. Unfortunately, the small form factor of these devices limits the light sensitivity, and often the the lens and sensor do not allow for satisfactory image quality. Matters become worse as each frame can be exposed for only a fraction of a second. Even for such short exposures, motion blur is still noticeable and may destroy visual details. In addition, the internal bandwidth of the storage unit inside the camera also puts a limit on resolution. Image quality may also suffer from the ability of the person that operates the camera. The dynamic range of current cameras is not high enough to correct over- and under-exposure afterward, and excessive shake increases motion blur. On the other hand, when taking photographs with a still camera, we are much less subject to these issues. We wish to process a video into a more aesthetically pleasing version, by borrowing information from high quality reference photographs of the same scene. Since the process of taking a photograph is not time-critical, we can afford a longer exposure for reducing noise, and record more information to increase resolution. Also, photographs are less prone to motion blur if a tripod is used.

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References

[1]
Freeman, W. T., Jones, T. R., and Pasztor, E. C. 2002. Example-based super-resolution. IEEE Computer Graphics and Applications 22, 2 (March/April), 56--65.
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Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., and Salesin, D. H. 2001. Image analogies. In SIGGRAPH 2001, Computer Graphics Proceedings, ACM Press / ACM SIGGRAPH, E. Fiume, Ed., 327--340.
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Kwatra, V., Schdl, A., Essa, I., Turk, G., and Bobick, A. 2003. Graphcut textures: Image and video synthesis using graph cuts. ACM Transactions on Graphics, SIGGRAPH 2003 22, 3 (July), 277--286.
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Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. In International Journal of Computer Vision, vol. 20, 91--110.

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cover image ACM Conferences
SIGGRAPH '07: ACM SIGGRAPH 2007 posters
August 2007
197 pages
ISBN:9781450318280
DOI:10.1145/1280720
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 05 August 2007

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