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An Adaptive Parameter Estimation in a BTV Regularized Image Super-Resolution ReconstructionMOFIDI, M.   , HAJGHASSEM, H. , AFIFI, A. |
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Author keywords
image processing, image reconstruction, maximum a posteriori, spatial resolution, statistical analysis
References keywords
image(35), resolution(24), super(23), process(16), signal(11), reconstruction(10), regularization(8), robust(6), restoration(6), processing(6)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03001
Web of Science Accession Number: 000410369500001
SCOPUS ID: 85028561933
Abstract
Access to the fine spatial resolution has always been a hotspot in digital imaging. One way to improve resolution is to use signal post-processing techniques. In this study, an improved multi-frame image super-resolution (SR) algorithm is proposed. The objective function should be minimized consists of a data error term, a regularization term and a regularization parameter. Based on the bilateral-total-variation (BTV) regularization, in the proposed method on one hand, the data error term incorporates frames with high accuracies in the reconstruction process, where an indicator weights each frame proportional to the frame error. On the other hand the regularization parameter is updated in each iteration based upon the Morozov's discrepancy principle. Iterative adjustment of the regularization parameter guarantees the SR solution to satisfy discrepancy principle. Visual evaluation and also quantitative measurements show that the performance of the proposed algorithm is better than of the several state-of-the-art methods. |
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| [1] S. C. Park, M. K. Park, M. G. Kang, "Super-resolution image reconstruction: a technical overview," IEEE Signal Process. Mag., vol. 20, no. 3, pp. 21-36, May 2003. [CrossRef] [Web of Science Times Cited 2273] [2] M. N. Bareja, C. K. Modi, "An improved iterative back projection based single image super resolution approach," Int. J. Image Graph., vol. 14, no. 04, p. 1450015, Oct. 2014. [CrossRef] [Web of Science Times Cited 7] [3] T. Lukes, K. Fliegel, M. Klima, "Objective image quality assessment of multiframe super-resolution methods," in Proc. 23rd International Conference Radioelektronika, Czech Republic, 2013, pp. 267-272. [CrossRef] [4] T. Wang, L. Cao, W. Yang, Q. Feng, W. Chen, Y. Zhang, "Adaptive patch-based POCS approach for super resolution reconstruction of 4D-CT lung data," Phys. Med. Biol., vol. 60, no. 15, pp. 5939-5954, Aug. 2015. [CrossRef] [Web of Science Times Cited 9] [5] P. Vandewalle, L. Sbaiz, J. Vandewalle, M. Vetterli, "Super-resolution from unregistered and totally aliased signals using subspace methods," IEEE Trans. Signal Process., vol. 55, no. 7, pp. 3687-3703, Jul. 2007. [CrossRef] [Web of Science Times Cited 74] [6] M. K. Ng, A. C. Yau, "Super-resolution image restoration from blurred low-resolution images," J. Math. Imaging Vis., vol. 23, no. 3, pp. 367-378, Nov. 2005. [CrossRef] [Web of Science Times Cited 13] [7] M. Shen, C. Wang, P. Xue, W. Lin, "Performance of reconstruction-based super-resolution with regularization," J. Vis. Commun. Image Represent., vol. 21, no. 7, pp. 640-650, 2010. [CrossRef] [Web of Science Times Cited 5] [8] L. T. Shao, H. G. Zhang, G. H. Zhang, "The improved hybrid MAP-POCS based algorithm for super-resolution image restoration research," Appl. Mech. Mater., vol. 701-702, pp. 373-380, Dec. 2014. [CrossRef] [9] S. D. Babacan, R. Molina, A. K. Katsaggelos, "Parameter estimation in TV image restoration using variational distribution approximation," IEEE Trans. Image Process., vol. 17, no. 3, pp. 326-339, Mar. 2008. [CrossRef] [Web of Science Times Cited 192] [10] P. Milanfar, "Super-Resolution Imaging", pp. 9-23, CRC Press, 2011. [11] V. Patanavijit, S. Jitapunkul, "A robust iterative multiframe super-resolution reconstruction using a Huber bayesian approach with Huber-Tikhonov regularization," in Proc. 2006 International Symposium on Intelligent Signal Processing and Communications, Yonago, Japan, 2006, pp. 13-16. [CrossRef] [12] V. Patanavijit, S. Jitapunkul, "A robust iterative multiframe super-resolution reconstruction using a bayesian approach with Tukey's biweight," in Proc. 2006 8th international Conference on Signal Processing, Beijing, China, 2006. [CrossRef] [13] V. Patanavijit, S. Jitapunkul, "A lorentzian stochastic estimation for a robust iterative multiframe super-resolution reconstruction with Lorentzian-Tikhonov regularization," EURASIP J. Adv. Signal Process., vol. 2007, no. 1, p. 034821, 2007. [CrossRef] [Web of Science Times Cited 37] [14] A. A. Hefnawy, "An efficient super-resolution approach for obtaining isotropic 3-D imaging using 2-D multi-slice MRI," Egypt. Informatics J., vol. 14, no. 2, pp. 117-123, 2013. [CrossRef] [Web of Science Times Cited 7] [15] Q. Yuan, L. Zhang, H. Shen, "Regional spatially adaptive total variation super-resolution with spatial information filtering and clustering," IEEE Trans. Image Process., vol. 22, no. 6, pp. 2327-2342, Jun. 2013. [CrossRef] [Web of Science Times Cited 56] [16] S. Farsiu, M. D. Robinson, M. Elad, P. Milanfar, "Fast and robust multiframe super resolution," IEEE Trans. Image Process., vol. 13, no. 10, pp. 1327-1344, Oct. 2004. [CrossRef] [Web of Science Times Cited 1522] [17] V. Moram, S. D. Cabrera, "Superresolution using the optimal recovery framework with automatic generalized cross-validation," in Proc. Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), USA, 2011, pp. 344-349. [CrossRef] [18] H. Z. Wang, S. Zhao, H.W. Lv, "Super-resolution image restoration with L-Curve," in Proc. 2008 Congress on Image and Signal Processing, China, 2008, pp. 597-601. [CrossRef] [19] Q. Yuan, L. Zhang, H. Shen, P. Li, "Adaptive multiple-frame image super-resolution based on U-curve," IEEE Trans. Image Process., vol. 19, no. 12, pp. 3157-3170, Dec. 2010. [CrossRef] [Web of Science Times Cited 60] [20] V. A. Morozov, "Methods for Solving Incorrectly Posed Problems", pp. 32-153, Springer Press, New York, 1984. [CrossRef] [21] D. Krawczyk-Stando, M. Rudnicki, "Regularization parameter selection in discrete ill-posed problems - the use of the U-Curve," Int. J. Appl. Math. Comput. Sci., vol. 17, no. 2, pp. 157-164, Jan. 2007. [CrossRef] [Web of Science Times Cited 94] [22] Y. W. Wen, R. H. Chan, "Parameter selection for total-variation-based image restoration using discrepancy principle," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1770-1781, Apr. 2012. [CrossRef] [Web of Science Times Cited 126] [23] C. He, C. Hu, W. Zhang, B. Shi, "A fast adaptive parameter estimation for total variation image restoration," IEEE Trans. Image Process., vol. 23, no. 12, pp. 4954-4967, Dec. 2014. [CrossRef] [Web of Science Times Cited 48] [24] M. V Afonso, J. M. Bioucas-Dias, M. A. T. Figueiredo, "An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems," IEEE Trans. Image Process., vol. 20, no. 3, pp. 681-695, Mar. 2011. [CrossRef] [Web of Science Times Cited 803] [25] P. Purkait, B. Chanda, "Super resolution image reconstruction through bregman iteration using morphologic regularization," IEEE Trans. Image Process., vol. 21, no. 9, pp. 4029-4039, Sep. 2012. [CrossRef] [Web of Science Times Cited 49] [26] A. Panagiotopoulou, V. Anastassopoulos, "Regularized super-resolution image reconstruction employing robust error norms," Opt. Eng., vol. 48, no. 11, p. 117004, Nov. 2009. [CrossRef] [Web of Science Times Cited 17] [27] X. Zeng, L. Yang, "A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization," Digit. Signal Process., vol. 23, no. 1, pp. 98-109, Jan. 2013. [CrossRef] [Web of Science Times Cited 59] [28] H. Song, L. Qing, Y. Wu, X. He, "Adaptive regularization-based space-time super-resolution reconstruction," Signal Process. Image Commun., vol. 28, no. 7, pp. 763-778, Aug. 2013. [CrossRef] [Web of Science Times Cited 16] [29] J. F. Aujol, G. Gilboa, "Constrained and SNR-based solutions for TV-Hilbert space image denoising," J. Math. Imaging Vis., vol. 26, no. 1-2, pp. 217-237, Nov. 2006. [CrossRef] [Web of Science Times Cited 52] [30] A. Panagiotopoulou, V. Anastassopoulos, "Super-resolution image reconstruction techniques: trade-offs between the data-fidelity and regularization terms," Inf. Fusion, vol. 13, no. 3, pp. 185-195, Jul. 2012. [CrossRef] [Web of Science Times Cited 22] [31] B. Setiyono, M. Hariadi, M. H. Purnomo, "Survey of super-resolution using phased based image matching," Journal of Theoretical and Applied Information Technology, Vol. 43, pp. 245-253, Sep. 2012. [32] J. Immerkær, "Fast noise variance estimation," Comput. Vis. Image Underst., vol. 64, no. 2, pp. 300-302, Sep. 1996. [CrossRef] [Web of Science Times Cited 366] [33] Z. Zheng Liu, R. Laganiere, "On the use of phase congruency to evaluate image similarity," in Proc. 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, France, 2006, pp. II-937-II-940. [CrossRef] [34] Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004. [CrossRef] [Web of Science Times Cited 31794] [35] X. Zhu, P. Milanfar, "Automatic parameter selection for denoising algorithms using a no-reference measure of image content," IEEE Trans. Image Process., vol. 19, no. 12, pp. 3116-32, Dec. 2010. [CrossRef] [Web of Science Times Cited 295] [36] N. D. Narvekar, L. J. Karam, "A no-reference image blur metric based on the cumulative probability of blur detection (CPBD)," IEEE Trans. Image Process., vol. 20, no. 9, pp. 2678-2683, Sep. 2011. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
