DocumentCode :
3241332
Title :
Selective Bayesian estimation for efficient super-resolution
Author :
Ivanovski, Zoran A. ; Karam, Lina J. ; Abousleman, Glen R.
Author_Institution :
Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA
fYear :
2004
fDate :
18-21 Dec. 2004
Firstpage :
433
Lastpage :
436
Abstract :
In this paper, a new approach to efficient and robust super-resolution is presented. Our method is based on selectively applying a Bayesian MAP estimator to image regions with high spatial activity. The degree of spatial activity is measured using the gradient of the estimated high-resolution image at each iteration. In addition, selective filtering is applied to enhance the visual quality of the estimated high-resolution image. The results obtained via simulation and with real video sequences demonstrate up to a 50% reduction in computational complexity, with improved visual quality, and higher SNR gains for magnification factors of four or more.
Keywords :
Bayes methods; filtering theory; image registration; image resolution; maximum likelihood estimation; MAP estimator; image filtering; image registration; maximum a posteriori estimator; robust super-resolution; selective Bayesian estimation; visual quality; Bayesian methods; Image registration; Interpolation; Maximum likelihood estimation; Pixel; Robustness; Spatial resolution; State estimation; Strontium; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Information Technology, 2004. Proceedings of the Fourth IEEE International Symposium on
Print_ISBN :
0-7803-8689-2
Type :
conf
DOI :
10.1109/ISSPIT.2004.1433811
Filename :
1433811
Link To Document :
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