[1] JIANG J, CHEN C, MA J, et al. SRLSP:a face image super-resolution algorithm using smooth regression with local structure prior[J]. IEEE Transactions on Multimedia, 2016, 19(1):27-40. [2] WU L, WANG Y. The process of criminal investigation based on grey hazy set[C]//Proceedings of the 2010 IEEE International Conference on Systems, Man and Cybernetics. Piscataway:IEEE, 2010:26-28. [3] EARLY D S, LONG D G. Image reconstruction and enhanced resolution imaging from irregular samples[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(2):291-302. [4] GREENSPAN H. Super-resolution in medical imaging[J]. The Computer Journal, 2009, 52(1):43-63. [5] WEI Z, MA K. Contrast-guided image interpolation[J]. IEEE Transactions on Image Processing, 2013, 22(11):4271-4285. [6] YANG W, LIU J, LI M, et al. Isophote-constrained autoregressive model with adaptive window extension for image interpolation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(5):1071-1086. [7] ROMANO Y, PROTTER M, ELAD M. Single image interpolation via adaptive nonlocal sparsity-based modeling[J]. IEEE Transactions on Image Processing, 2014, 23(7):3085-3098. [8] YE W, MA K. Convolutional edge diffusion for fast contrast-guided image interpolation[J]. IEEE Signal Processing Letters, 2016, 23(9):1260-1264. [9] CAO F, CAI M, TAN Y. Image interpolation via low-rank matrix completion and recovery[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(8):1261-1270. [10] HUANG J, SIU W, LIU T. Fast image interpolation via random forests[J]. IEEE Transactions on Image Processing, 2015, 24(10):3232-3245. [11] ZHU S, ZENG B, ZENG L, et al. Image interpolation based on non-local geometric similarities and directional gradients[J]. IEEE Transactions on Multimedia, 2016, 18(9):1707-1719. [12] MARQUINA A, OSHER S J. Image super-resolution by TV-regularization and Bregman iteration[J]. Journal of Scientific Computing, 2008, 37(3):367-382. [13] FERNANDEZ-GRANDA C, CANDÈS E J. Super-resolution via transform-invariant group-sparse regularization[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2013:3336-3343. [14] 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):1838-1857. [15] 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. [16] SUN J, XU Z, SHUM H Y. Image super-resolution using gradient profile prior[C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2008:1-8. [17] WANG L, XIANG S, MENG G, et al. Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(8):1289-1299. [18] GAO X, ZHANG K, TAO D, et al. Image super-resolution with sparse neighbor embedding[J]. IEEE Transactions on Image Processing, 2012, 21(7):3194-3205. [19] YANG J, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11):2861-2873. [20] TIMOFTE R, de SMET V, van GOOL L. Anchored neighborhood regression for fast example-based super-resolution[C]//Proceedings of the IEEE 2013 International Conference on Computer Vision. Piscataway:IEEE, 2013:1920-1927. [21] ZHAO J, HU H, CAO F. Image super-resolution via adaptive sparse representation[J]. Knowledge-Based Systems, 2017, 124:23-33. [22] HUANG K, HU R, JIANG J, et al. Face image super-resolution through improved neighbor embedding[C]//Proceedings of the 2016 International Conference on Multimedia Modeling, LNCS 9516. Cham:Springer, 2016:409-420. [23] PARK J S, SOH J W, CHO N I. High dynamic range and super-resolution imaging from a single image[J]. IEEE Access, 2018, 6:10966-10978. [24] CHANG H, YEUNG D Y, XIONG Y. Super-resolution through neighbor embedding[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2004:I-I. [25] ZHANG K, TAO D, GAO X, et al. Learning multiple linear mappings for efficient single image super-resolution[J]. IEEE Transactions on Image Processing, 2015, 24(3):846-861. [26] JIANG J, MA X, CHEN C, et al. Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means[J]. IEEE Transactions on Multimedia, 2017, 19(1):15-26. [27] TIMOFTE R, ROTHE R, van GOOL L. Seven ways to improve example-based single image super resolution[C]//Proceedings of the IEEE 2016 Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:1865-1873. [28] DOU Q, WEI S, YANG X, et al. Medical image super-resolution via minimum error regression model selection using random forest[J]. Sustainable Cities and Society, 2018, 42:1-12. [29] ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations[C]//Proceedings of the 2010 International Conference on Curves and Surfaces, LNCS 6920. Berlin:Springer, 2010:711-730. [30] BEVILACQUA M, ROUMY A, GUILLEMOT C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[EB/OL].[2019-01-12]. http://people.rennes.inria.fr/Aline.Roumy/publi/12bmvc_Bevilacqua_lowComplexitySR.pdf. [31] DONG C, LOY C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2):295-307. |