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Image Resolution Enhancement Using Bayesian Inla Approximation

M. Hemalatha1

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-04 , Page no. 134-136, Feb-2019

Online published on Feb 28, 2019

Copyright © M. Hemalatha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: M. Hemalatha, “Image Resolution Enhancement Using Bayesian Inla Approximation,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.134-136, 2019.

MLA Style Citation: M. Hemalatha "Image Resolution Enhancement Using Bayesian Inla Approximation." International Journal of Computer Sciences and Engineering 07.04 (2019): 134-136.

APA Style Citation: M. Hemalatha, (2019). Image Resolution Enhancement Using Bayesian Inla Approximation. International Journal of Computer Sciences and Engineering, 07(04), 134-136.

BibTex Style Citation:
@article{Hemalatha_2019,
author = {M. Hemalatha},
title = {Image Resolution Enhancement Using Bayesian Inla Approximation},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {07},
Issue = {04},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {134-136},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=736},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=736
TI - Image Resolution Enhancement Using Bayesian Inla Approximation
T2 - International Journal of Computer Sciences and Engineering
AU - M. Hemalatha
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 134-136
IS - 04
VL - 07
SN - 2347-2693
ER -

           

Abstract

Super-resolution (SR) is a technique to enhance the resolution of an image without changing the camera resolution, through using software algorithms. In this context, this paper proposes a fully automatic SR algorithm, using a recent nonparametric Bayesian inference method based on numerical integration, known in the statistical literature as integrated nested Laplace approximation (INLA). By applying such inference method to the SR problem, this paper shows that all the equations needed to implement this technique can be written in closed form. Moreover, the results of several simulations show that the proposed algorithm performs better than other SR algorithms recently proposed.

Key-Words / Index Term

Bayesian inference, Closed form, Integrated Nested Laplace Approximation (INLA), Nonparametric, Super-resolution (SR)

References

[1] N. P. Galatsanos, V. Z. Mesarovic, R. Molina, and A. K. Katsaggelos, “Hierarchical Bayesian image restoration from partially-known blurs,” IEEE Trans. Image Process., vol. 9, no. 10, pp. 1784–1797, Oct. 2000.
[2] M. Tipping and C. Bishop, “Bayesian image super-resolution,” in Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, pp. 1303–1310, 2003.
[3] T. Pham, L. Van Vliet, and K. Schutte, “Robust fusion of irregularly sampled data using adaptive normalized convolution,” EURASIP J. Appl.Signal Process., vol. 2006, pp. 1–12, Jan. 2006.
[4] M. Vetterli, “Superresolution images reconstructed from aliased images,” in Proceedings of SPIE/IS&T Visual Communications and Image Processing Conference, T. Ebrahimi and T. Sikora, Eds., vol. 5150 of Proceedings of SPIE, Switzerland, 2003.
[5] P. Vandewalle, S. Süsstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to superresolution,” EURASIP J. Appl. Signal Process, vol. 2006, pp. 1–14, Mar. 2006.
[6] M. Irani and S. Peleg, “Motion analysis for image enhancement: Resolution, occlusion, and transparency,” J. Visual Commun. Image Represent., vol. 4, no. 4, pp. 324–335, 1993.
[7] A. Patti, M. Sezan, and A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process., vol. 6, no. 8, pp. 1064–1076, Aug. 1997.
[8] B. Tom and A. Katsaggelos, “An iterative algorithm for improving the resolution of video sequences,” Proc. SPIE Int. Soc. Opt. Eng., vol. 2727, no. 3, pp. 1430–1438, 1996.