Opto-Electronic Engineering, Volume. 46, Issue 11, 180499(2019)
An anisotropic edge total generalized variation energy super-resolution based on fast l1-norm dictionary edge representations
[1] [1] Maalouf A, Larabi M C. Colour image super-resolution using geometric grouplets[J]. IET Image Processing, 2012, 6(2): 168–180.
[5] [5] Ng M K, Shen H F, Lam E Y, et al. A total variation regulariza-tion based super-resolution reconstruction algorithm for digital video[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2007: 074585.
[6] [6] Marquina A, Osher S J. Image super-resolution by TV-regularization and Bregman iteration[J]. Journal of Scientific Computing, 2008, 37(3): 367–382.
[7] [7] Li X L, Hu Y T, Gao X B, et al. A multi-frame image su-per-resolution method[J]. Signal Processing, 2010, 90(2): 405–414.
[8] [8] Farsiu S, Robinson M D, Elad M, et al. Fast and robust multi-frame super resolution[J]. IEEE Transactions on Image Processing, 2004, 13(10): 1327–1344.
[9] [9] Yue LW, Shen H F, Yuan Q Q, et al. A locally adaptive L1-L2 norm for multi-frame super-resolution of images with mixed noise and outliers[J]. Signal Processing, 2014, 105: 156–174.
[10] [10] Wang L F, Xiang S M, Meng G F, et al. Edge-directed sin-gle-image super-resolution via adaptive gradient magnitude self-interpolation[J]. IEEE Transactions on Circuits and Sys-tems for Video Technology, 2013, 23(8): 1289–1299.
[11] [11] Sun J, Sun J, XuZ B, et al. Gradient profile prior and its appli-cations in image super-resolution and enhancement[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1529–1542.
[12] [12] Feng W S, Lei H. Single-image super-resolution with total generalised variation and Shearlet regularisations[J]. IET Im-age Processing, 2014, 8(12): 833–845.
[13] [13] Ma Z Y, Liao RJ, Tao X, et al. Handling motion blur in mul-ti-frame super-resolution[C]//Proceedings of 2015 IEEE Con-ference on Computer Vision and Pattern Recognition, 2015: 5224–5232.
[14] [14] Yang J C, Wright J, Huang T, et al. Image super-resolution as sparse representation of raw image patches[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Rec-ognition, 2008: 1–8.
[15] [15] Yang J C, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861–2873.
[16] [16] He L, Qi H R, Zaretzki R. Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013: 345–352.
[17] [17] Liu W R, Li S T. Sparse representation with morphologic regu-larizations for single image super-resolution[J]. Signal Processing, 2014, 98: 410–422.
[18] [18] Lu C W, Shi J P, Jia J Y. Online robust dictionary learn-ing[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013: 415–422.
[19] [19] Ferstl D, Reinbacher C, Ranftl R, et al. Image guided depth upsampling using anisotropic total generalized varia-tion[C]//Proceedings of 2013 IEEE International Conference on Computer Vision, 2013: 993–1000.
[20] [20] Ferstl D, Ruther M, Bischof H. Variational depth superresolution using example-based edge representations[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 513–521.
[21] [21] Dong C, Loy C C, He K M, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295–307.
[22] [22] Bredies K, Kunisch K, Pock T. Total generalized variation[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 492–526.
[23] [23] Benning M, Brune C, Burger M, et al. Higher-order TV me-thods-enhancement via Bregman iteration[J]. Journal of Scien-tific Computing, 2013, 54(2–3): 269–310.
[24] [24] Bissantz N, Dümbgen L, Munk A, et al. Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces[J]. SIAM Journal on Optimization, 2009, 19(4): 1828–1845.
[25] [25] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Rec-ognition, 2016: 1646–1654.
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Mu Shaoshuo, Zhang Jiefang. An anisotropic edge total generalized variation energy super-resolution based on fast l1-norm dictionary edge representations[J]. Opto-Electronic Engineering, 2019, 46(11): 180499
Category: Article
Received: Sep. 26, 2018
Accepted: --
Published Online: Dec. 8, 2019
The Author Email: Shaoshuo Mu (hitshaoshuomu@163.com)