Image super-resolution reconstruction algorithm based on fields of experts prior model

Zhang Xiu; Zhou Wei; Duan Zhemin; Wei Henglu

doi:10.3788/irla2019478.0626002

[1] Park S C, Park M K, Kang M G. Super-resolution image reconstruction: A technical overview[J]. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.

[2] Boccacci P , Bertero A M . Introduction to Inverse Problems in Imaging[M]. Korea: Research Center for Peace and Unification of Korea, 1987: 46-47.

[3] Elad M, Feuer A. Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images[J]. IEEE Transactions on Image Processing A, 1997, 6(12): 1646-1658.

[4] Irani M, Peleg S. Improving resolution by image registration[J]. Cvgip Graphical Models & Image Processing, 1991, 53(3): 231-239.

[5] Baker S, Kanade T. Limits on super-resolution and how to break them[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(9): 0-1183.

[6] Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution[J]. Computer Graphics & Applications IEEE, 2002, 22(2): 56-65.

[7] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

[8] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006, 15(12): 3736-45.

[9] Dong W, Zhang L, Shi G. Centralized sparse representation for image restoration[C]//IEEE International Conference on Computer Vision, 2012.

[10] Dong W, Zhang L, Shi G, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2013, 22(4): 1620-1630.

[11] Mairal J, Bach F, Ponce J, et al. Non-local sparse models for image restoration[C]//IEEE, International Conference on Computer Vision, ICCV 2009, 2009: 2272-2279.

[12] Ebrahimi M. Inverse problems and self-similarity in imaging [D]. Waterloo: University of Waterloo, 2008.

[13] Elad M, Datsenko D. Corrigendum: example-based regularization deployed to super-resolution reconstruction of a single image [J]. Computer Journal, 2009, 52(4): 15-30.

[14] Li S Z. Markov Random Field Modeling in Iimage Analysis [M]. New York: Springer-Verlag, 2001.

[15] Roth S, Black M J. Fields of Experts: A framework for learning image priors[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 860-867.

[16] Roth S, Black M J. Fields of experts[J]. International Journal of Computer Vision, 2009, 82(2): 205-229.

[17] Welling M, Hinton G, Osindero S. Learning sparse topographic representations with products of student-t distribution[C]//Advances in Neural Information Processing Systems(NIPS′03), 2003: 1359-1366.

[18] Dong W, Zhang L, Shi G, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J]. IEEE Transactions on Image Processing, 2011, 20(7): 2108306.