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CLC number: TN911.73

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Received: 2009-01-06

Revision Accepted: 2009-04-24

Crosschecked: 2009-10-18

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.12 P.1705-1713

http://doi.org/10.1631/jzus.A0920013


A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution


Author(s):  Wen CHEN, Xiang-zhong FANG, Yan CHENG

Affiliation(s):  Shanghai Key Lab of Digital Media Processing and Transmissions, Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   chenwen_01@yahoo.com.cn

Key Words:  Edge preservation, Multidimensional Lorentzian distribution (MDL), Super resolution, Threshold


Wen CHEN, Xiang-zhong FANG, Yan CHENG. A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution[J]. Journal of Zhejiang University Science A, 2009, 10(12): 1705-1713.

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DOI - 10.1631/jzus.A0920013


Abstract: 
This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.

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Reference

[1] Baker, S., Kanade, T., 2002. Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell., 24(9):1167-1183.

[2] Chantas, G.K., Galatsanos, N.P., Woods, N.A., 2007. Super-resolution based on fast registration and maximum a posteriori reconstruction. IEEE Trans. Image Process., 16(7):1821-1830.

[3] Cheng, Y., Fang, X.Z., Yang, X.K., 2007. Edge-image-based approach for stable super-resolution reconstruction. Opt. Eng., 46(2):027004.

[4] Donaldson, K., Myers, G.K., 2005. Bayesian super-resolution of text in video with a text-specific bimodal prior. Int. J. Docum. Anal. Recogn., 7(2-3):159-167.

[5] El-Yamany, N.A., Papamichalis, P.E., 2008. Robust color image super-resolution: an adaptive M-estimation framework. EURASIP J. Image Video Process., 2008(2):38052.

[6] Haber, E., Tenorio, L., 2003. Learning regularization functionals: a supervised training approach. Inv. Probl., 19(3):611-626.

[7] Hardie, R.C., Barnard, K.J., Armstrong, E.E., 1997. Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Process., 6(12):1621-1633.

[8] Irani, M., Peleg, S., 1991. Improving resolution by image registration. CVGIP: Graph. Models Image Process., 53(3):231-239.

[9] Kim, S.P., Bose, N.K., Valenzuela, H.M., 1990. Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Trans. Acoust., Speech, Signal Process., 38(6):1013-1027.

[10] Lettington, A.H., Hong, Q.H., 1995. Ringing artifact reduction for Poisson MAP superresolution algorithms. IEEE Signal Process. Lett., 2(5):83-84.

[11] Lettington, A.H., Tzimopoulou, S., Rollason, M.P., 2001. Nonuniformity correction and restoration of passive millimeter-wave images. Opt. Eng., 40(2):268-274.

[12] Nielsen, M., 1997. Graduated nonconvexity by functional focusing. IEEE Trans. Pattern Anal. Mach. Intell., 19(5):521-525.

[13] Park, S.C., Park, M.K., Kang, M.G., 2003. Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag., 20(3):21-36.

[14] Patanaviji, V., Tae-O-Sot, S., Jitapunkul, S., 2007. A Robust Iterative Super-resolution Reconstruction of Image Sequences Using a Lorentzian Bayesian Approach with Fast Affine Block-based Registration. IEEE Int. Conf. on Image Processing, p.393-396.

[15] Schultz, R.R., Stevenson, R.L., 1996. Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process., 5(6):996-1011.

[16] Segall, C.A., Katsaggelos, A.K., Molina, R., Mateos, J., 2004. Bayesian resolution enhancement of compressed video. IEEE Trans. Image Process., 13(7):898-911.

[17] Stark, H., Oskoui, P., 1989. High resolution image recovery from image-plane arrays, using convex projections. J. Opt. Soc. Am. A, 6(11):1715-1726.

[18] Suh, J.W., Jeong, J., 2004. Fast sub-pixel motion estimation techniques having lower computational complexity. IEEE Trans. Consum. Electron., 50(3):968-973.

[19] Tsai, R.Y., Huang, T.S., 1984. Multi-frame image restoration and registration. Adv. Comput. Vis. Image Process., 1(2):317-339.

[20] Zhu, H., Lu, Y., Wu, Q., 2007. Super-resolution Image Restoration by Maximum Likelihood Method and Edge-oriented Diffusion. SPIE, 6625:66250Y-1-8.

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