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High resolution images from low resolution video sequences
Keywords:
Super Resolution, Image Enhancement, Image ProcessingAbstract
In some cases, low resolution of those images composing a video film hinders the proper visual interpretation of its data. A typical example of this is video obtained from security cameras. There thus exists the need to count with some method allowing the processing of such information in order to obtain a better quality and a higher level of detail of those images. This gives rise to the possibility of making a more reliable interpretation of images, all of which eases the determination of, for example, some people face features or a car plate numbers. Nowadays, there exist some techniques that are related to this topic (called Image Super- Resolution techniques), though in the theoretical field in principle. Besides, there is no integral solution presented as integral product for its utilization. This paper presents the preliminary results of the Super-Resolution techniques applied to video sequences with the possibility of using quality enhancement preprocessing in each individual image.
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References
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[2] Super-Resolution Reconstruction of Images - Static and Dynamic Paradigms. Michael Elad. http://www.cs.technion.ac.il/~elad/Lectures/Super-Resolution_All.ppt
[3] MAP Based Resolution Enhancement of Video Sequences Using a Huber-Markov Random Field Image Prior Model. Hu He, Lisimachos P. Kondi. IEEE International Conference on Image Processing, Barcelona, Spain, September 2003, Vol. II, pp. 933-936
[4] Choice of threshold of the Huber-Markov prior in mapbased video resolution enhancement. Hu He, Lisimachos P. Kondi. IEEE Canadian Conference on Electrical and Computer Engineering, Niagara Falls, Canada, May 2004.
[5] Extraction of High-Resolution Frames from Video Sequences. Richard R. Schultz. Robert L. Stevenson. IEEE T. Image Proces., 5(6), pp. 996--1011, 1996.
[6] A Bayesian Approach to Image Expansion for Improved Definition Richard R. Schultz. Robert L. Stevenson. IEEE Transactions on Image Processing, 3(3):233-242, May 1994.
[7] Aplicaciones de los algoritmos de restauración de imágenes multicanal a problemas de súper – resolución Javier Mateos. http://decsai.ugr.es/vip/files/presentations/2003_mateos.pdf
[8] Simultaneous Parameter Estimation and Segmentation of Gibbs Random Field using Simulated Annealing Sridhar Lakshmanan. Haluk Derin. IEEE Transactions on Pattern Analysis and Machine Intelligence, v.11 n.8, p.799-813, August 1989
[9] Search Algorithms for Block-Matching in Motion Estimation. Deepak Turaga. Mohamed Alkanhal. Mid-Term project. 18-899. Spring, 1998
[10] Block Matching for Object Tracking A. Gyaourova. C. Kamath. SC. Cheung. UCRLTR-200271. 2003.
[11] Motion Estimation and Hybrid Video Coding Based on Block-M.E. Min Wu. ENEE631. Lecture13.
[12] Markov Random Fields with Applications to M-reps Models. Conglin Lu. http://www.cs.unc.edu/Research/MIDAG/pubs/presentations/StatsGeomTuts/LuGeomStats.pdf
[13] Super-Resolved Surface Reconstruction From Multiple Images Peter Cheeseman. Bob Kanefsky. Richard Kraft. John Stutz. Technical Report FIA–94–12
[14] Súper resolución de imágenes y vídeo Rafael Molina. http://decsai.ugr.es/vip/doctorado/pvd/ T11c.pdf
[15] Image Inpainting Marcelo Bertalmío. Guillermo Sapiro. Vicent Caselles. Coloma Ballester www.iua.upf.es/~mbertalmio/bertalmi.pdf
[16] Digital Image Processing. R.C. Gonzalez. R.E. Woods. Addison Wesley. 1992
[17] Processing of Flat and Non-Flat Image Information On Arbitrary Manifolds Using Partial Differential Equations. Marcelo Bertalmío. http://iie.fing.edu.uy/investigacion/grupos/gti/biblio/doctorado_mbertalmio.pdf
[18] Image/Video Resolution Enhancement Technique from a Sequence of Low-Resolution Images using Joint MAP Registration Algorithm. Hu He. eureka.eng.buffalo.edu/map.pdf
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Published
2005-04-01
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[1]
“High resolution images from low resolution video sequences”, JCS&T, vol. 5, no. 01, pp. p. 30–36, Apr. 2005, Accessed: Mar. 08, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/885
