Super-resolution

This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations where appropriate. (March 2008)

An example of super-resolution processing. The left half shows the original, blown up to twice its normal size. The right half shows the image (at native resolution) that results when the software combines nine images together and performs 2x superresolution.

Super-resolution (SR) are techniques that enhance the resolution of an imaging system. Some SR techniques break the diffraction-limit of systems, while other SR techniques improve over the resolution of digital imaging sensor.

There are both single-frame and multiple-frame variants of SR. Multiple-frame SR use the sub-pixel shifts between multiple low resolution images of the same scene. They create an improved resolution image fusing information from all low resolution images, and the created higher resolution images are better descriptions of the scene. Single frame SR methods attempt to magnify the image without introducing blur. These methods use other parts of the low resolution images, or other unrelated images, to guess what the high resolution image should look like. Algorithms can also be divided by their domain: frequency or space domain.

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, nor does a system where the total system Modulation Transfer Function 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 radar, astronomy or microscopy.

See also

• optical resolution

References

• Z. Zalevsky and D. Mendlovic (2003), Optical Superresolution, Springer. ISBN 0-387-00591-9
• J.N. Caron, "Rapid supersampling of multiframe sequences by use of blind deconvolution," Optics Letters, September, 2004, Vol 29, No. 17, p. 1986-1988.
• G.T. Clement, J. Huttunen, and K. Hynynen, "Superresolution ultrasound imaging using back-projected reconstruction" Journal of the Acoustical Society of America, Volume 118, Issue 6, pp. 3953-3960, 2005.
• 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
• 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.
• M. Irani and S. Peleg, "Super Resolution From Image Sequences", ICPR, 2:115--120, June 1990.
• F. Sroubek, G. Cristobal, and J. Flusser, "A Unified Approach to Superresolution and Multichannel Blind Deconvolution", IEEE Trans. Image Processing, vol. 16, pp. 2322-2332, 2007

Other related work

• W.-S. Chan, E. Lam, M. Ng, and G. Mak, "Super-resolution reconstruction in a computational compound-eye imaging system,” Multidimensional Systems and Signal Processing, 18(2–3), pp. 83-101, 2007. [1]
• M. Ng, H. Shen, E. Lam, and L. Zhang, “A total variation regularization based super-resolution reconstruction algorithm for digital video,” EURASIP Journal on Advances in Signal Processing, ID 74585, 2007. [2]
• D. Glasner, S. Bagon, and M. Irani, "Super-Resolution from a Single Image", International Conference on Computer Vision (ICCV), October 2009. example and results

External links

• UCSC-MDSP RESOLUTION ENHANCEMENT SOFTWARE — A MATLAB implementation of several algorithms on Color and B&W data sets.
• SR GUI in Matlab — Super-resolution software that includes both geometric and photometric registration (only for Windows).
• Video super-resolution using video epitomes.
• EPFL-LCAV — a MATLAB implementation of several SR algorithms.
• What is super-resolution? – an article with step-by-step example of 4x video upsizing using super resolution approach.