Super-resolution (SR) are techniques that in some way enhance the resolution of an imaging system. There are different views as to what is considered SR-techniques though: some consider only techniques that break the diffraction-limit of systems, while others also consider techniques that merely break the limit of the digital imaging sensor as SR.

There are both single-frame and multiple-frame variants of SR, where multiple-frame are the most useful. Algorithms can also be divided by their domain: frequency or spatial domain. By fusing together several low-resolution (LR) one enhanced-resolution image is formed.

The necessity of aliasing

In the most common SR algorithms, the information that was gained in the SR-image was embedded in the LR images in the form of aliasing. This requires that the capturing sensor in the system is weak enough so that aliasing is actually happening. A diffraction-limited system contains no aliasing, for example, or a system where the total system MTF is filtering out high-frequency content.

Breaking the diffraction limit

There are also SR techniques that extrapolate the image in the frequency domain, by assuming that the object on the image is an analytic function, and that we can exactly know the function values in some interval. This method is severely limited by the noise that is ever-present in digital imaging systems, but it can work for astronomical or microscopial work.

See also

• Optics
• Fourier analysis
• Aliasing
• Epitome

References

• V. Cheung, B. J. Frey, and N. Jojic. "Video epitomes". In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2005.
• M. Bertero and P. Boccacci. "Super-resolution in computational imaging". Micron, 34:265–273, October 2003.
• S. Borman and R. Stevenson. Super-resolution from image sequences - a review. Technical report, University of Notre Dame, 1998.
• S. C. Park, M. K. Park, and M. G. Kang., "Super-resolution image reconstruction: a technical overview", IEEE Signal Processing Magazine, 20(3):21–36, May 2003.
• S. Farsiu, D. Robinson, M. Elad, and P. Milanfar. "Advances and Challenges in Super-Resolution", International Journal of Imaging Systems and Technology, Volume 14, no 2, pp. 47-57, August 2004
• Richardson, W.H. (1972), “Bayesian-Based Iterative Method of Image Restoration”, J. Opt. Soc. Am., 62, 55.
• Lucy, L.B. (1974), “An iterative technique for the rectification of observed distributions”, Astron. J., 79, 745.
• M. Elad and Y. Hel-Or, "Fast Super-Resolution Reconstruction Algorithm for Pure Translational Motion and Common Space-Invariant Blur ", IEEE Trans. on Image Processing, Vol 10, No 8, pp. 1187-1193, August 2001.

External links

• http://www.psi.toronto.edu/~vincent/videoepitome.html Video super-resolution using video epitomes.
• http://auricle.dyndns.org/ALE/ One implementation of SR-algorithms: this uses for example the IBP (Iterated BackProjection) method of Irani/Peleg.
• http://www.soe.ucsc.edu/~milanfar/SR-Software.htm A free SR Software implementing several algorithms on Color and BW data sets.
• http://www.photoacute.com/ A SR Software implementing superresolution algorithm based on correlating several digital images and optical system-dependent interpolation.

Image processing | Digital signal processing | Signal processing