Super-resolution
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. Originally super-resolution methods worked well only on grayscale images, but researchers have found methods to adapt them to color camera images.[1] Recently also the use of super-resolution for 3D data has been shown [2]
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
References
- Craig H. Curtis, Tom D. Milster (1992), "Analysis of Superresolution in Magneto-Optic Data Storage Devices," APPLIED OPTICS, October 1992, Vol. 31, No. 29, pp. 6272–6279.
- 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. Spatial Resolution Enhancement of Low-Resolution Image Sequences -- A Comprehensive Review with Directions for Future Research", Technical report, University of Notre Dame, 1998.
- S. Borman and R. Stevenson. "Super-resolution from image sequences — a review" in Proc. Midwest Symposium on Circuits and Systems, 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
- A. Calabuig, V. Mico, J. Garcia, Z. Zalevsky, and C. Ferreira, “Single-exposure super-resolved interferometric microscopy by RGB-multiplexing,” Opt. Lett. 36, 885-887 (2011)
- ^ S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, "Fast and Robust Multi-frame Super-resolution", IEEE Transactions on Image Processing, vol. 13, no. 10, pp. 1327-1344, October 2004.
- ^ S. Schuon, C. Theobalt, J. Davis, and S. Thrun, "LidarBoost: Depth Superresolution for ToF 3D Shape Scanning", In Proceedings of IEEE CVPR 2009
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