Synonyms
Definition
Image and video super resolution techniques refer to creating higher resolution image from single/multiple low-resolution input.
Introduction
Images with high pixel density are desirable in many applications, such as high-resolution (HR) medical images for medical diagnosis, high quality video conference, high definition Television broadcasting, Blu-ray movies, etc. While people can use higher resolution camera for the purpose, there is an increasing demand to shoot HR image/video from low-resolution (LR) cameras such as cell phone camera or webcam, or converting existing standard definition footage into high definition video material. Hence, software resolution enhancement techniques are very desirable for these applications.
The task of software image resolution enhancement is to estimate more pixel values to generate a processed image with higher resolution. The simplest way to produce more pixel values is to use up-sampling...
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References
M. Unser, A. Aldroubi, and M. Eden, “Enlargement or reduction of digital images with minimum loss of information,” IEEE Transactions on Image Process, No. 3, Mar. 1995, pp. 247–258.
R. Crochiere and L. Rabiner, “Interpolation and decimation of digital signals – a turorial review,” Proceedings of IEEE, No. 3, pp. 300–331, Mar. 1981.
S.C. Park, M.K. Park, and M.G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Processing Magazine, pp. 21–36, 2003.
R. Tsai and T. Huang, “Multiframe image restoration and registration,” Advances in Computer Vision and Image Processing (JAI Press), pp. 317–339, 1984.
S. Borman and R. Stevenson, “Super-resolution from image sequences-a review,” Proc. of Midwest Symposium on Circuits and Systems, pp. 374–378, 1998.
C. Jiji and C. Subhasis, “Single-frame image super-resolution through contourlet learning,” EURASIP Journal on Applied Signal Processing, p. 73767(11), 2006.
M.K. Ng and N.K. Bose, “Analysis of displacement errors in high-resolution image reconstruction with multisensors,” IEEE Transactions on Circuits and Systems (Part I), No. 6, pp. 806–813, 2002.
M.K. Ng and N.K. Bose, “Fast color image restoration with multisensors,” International Journal of Imaging Systems and Technoloy, No. 5, pp. 189–197, 2002.
N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Transactions on Image Processing, No. 4, pp. 573–583, 2001.
R.R. Schultz and R.L. Stevenson, “A bayesian approach to image expansion for improved definition,” IEEE Transactions on Image Processing, No. 3, pp. 233–242, 1994.
D. Rajan and S. Chaudhuri, “An mrf-based approach to generation of super-resolution images from blurred observations,” Journal of Mathematical Imaging and Vision, No. 1, pp. 5–15, 2002.
M. Elad and A. Feuer, “Restoration of a single super-resolution image from several blurred, noisy and undersampled measured images,” IEEE Transactions on Image Processing, No. 12, pp. 1646–1658, 1997.
N. Nguyen, P. Milanfar, and G. Golub, “Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement,” IEEE Transactions on Image Processing, pp. 1299–1308, Sept. 2001.
M.K. Ng, J. Koo, and N.K. Bose, “Constrained total leastsquares computations for high-resolution image reconstruction with multisensors,” International Journal of Imaging Systems and Technology, No. 1, 2002, pp. 35–42.
S. Borman and R. Stevenson, “Spatial resolution enhancement of low-resolution image sequences. a comprehensive review with directions for future research,” Laboratory Image and Signal Analysis, University of Notre Dame, Technical Report, 1998.
M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP: Graphical Models and Image Processing, No. 3, 1991, pp. 231–239.
M. Irani and S. Peleg, “Motion analysis for image enhancement: resolution, occlusion, and transparency,” Journal of Visual Communication and Image Representation, No. 4, 1993, p. 324–335.
W.T. Freeman, E.C. Pasztor, and O.T. Carmichael., “Learning low-level vision,” IJCV, No. 1, 2000, pp. 25–47.
J. Sun, H. Tao, and H. Shum, “Image hallucination with primal sketch priors,” Proceedings of the IEEE CVPR’03, pp. 729–736, 2003.
C.V. Jiji, M.V. Joshi, and S. Chaudhuri, “Single-frame image super-resolution using learned wavelet coefficients,” International Journal of Imaging Systems and Technology, No. 3, 2004, pp. 105–112.
D. Capel and A. Zisserman, “Super-resolution from multiple views using learnt image models,” Proceedings of the IEEE CVPR’01, pp. 627–634, December 2001.
Q. Wang, X. Tang, and H. Shum, “Patch based blind image super resolution,” Proceedings of ICCV’05, No. 1, pp. 709–716, 2005.
D.Y.H. Chang and Y. Xiong, “Super-resolution through neighbor embedding,” Proceedings of CVPR’04, 2004, pp. 275–282.
B. Gunturk, A. Batur, Y. Altunbasak, M. Hayes, and R.M. Mersereau, “Eigenface-domain super-resolution for face recognition,” IEEE Transactions on Image Processing, no. 5, 2003, pp. 597–606.
X. Wang and X. Tang, “Hallucinating face by eigentransformation with distortion reduction,” Proceedings of ICBA’04, pp. 88–94, 2004.
C.V. Jiji and S. Chaudhuri, “Pca-based generalized interpolation for image super-resolution,” Proceedings of Indian Conference on Vision, Graphics & Image Processing’04, pp. 139–144, 2004.
G. Dalley, B. Freeman, and J. Marks, “Single-frame text super-resolution: a bayesian approach,” Proceedings of IEEE ICIP’04, pp. 3295–3298, 2004.
D. Kong, M. Han, W. Xu, H. Tao, and Y. Gong, “A conditional random field model for video super-resolution,” Proceedings of ICPR’06, pp. 619–622, 2006.
Z. Lin and H. Shum, “Fundamental limits of reconstruction-based super-resolution algorithms under local translation,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), no. 1, 2004, pp. 83–97.
M. Ben-Ezra, A. Zomet, and S. Nayar, “Jitter camera: High resolution video from a low resolution detector,” Proceedings of IEEE CVPR’04, pp. 135–142, Jun. 2004.
C. Bishop, A. Blake, and B. Marthi, “Super-resolution enhancement of video,” Proceedings of the Artificial Intelligence and Statistics, 2003.
C. Williams and C. Rasmussen, “Gaussian processes for regression,” Advances in Neural Information Processing Systems, MIT Press., Cambridge, MA, 1996, pp. 514–520.
S. Dai, M. Han, W. Xu, Y. Wu, and Y. Gong, “Soft edge smoothness prior for alpha channel super resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, 2007.
R. Hardie, K. Barnard, and J. Bognar, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Optical Engineering, No. 1, Jan. 1998, pp. 247–260.
S. Farsiu, M.Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Transactions on Image Processing, pp. 1327–1344, 2004.
Levin, D. Lischinski, and Y. Weiss, “A closed form solution to natural image matting,” Proceedings of the IEEE CVPR’06, 2006.
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Wang, J., Gong, Y. (2008). Image and Video Super Resolution Techniques. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_27
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DOI: https://doi.org/10.1007/978-0-387-78414-4_27
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