[1] |
FREEMAN W T, PASZTOR E C, CARMICHAEL OT. Learning low-level vision [J]. International Journalof Computer Vision, 2000, 40(1): 25-47.
|
[2] |
TANG S Z, XIAO L, LIU P F, et al. Coupled learningbased on singular-values-unique and hog for facehallucination [C]//Proceedings of IEEE InternationalConference on Acoustics, Speech and Signal Processing.Brisbane, Australia: IEEE, 2015: 1315-1319.
|
[3] |
JIANG J J, HU R M, WANG Z Y, et al. Noise robustface hallucination via locality-constrained representation[J]. IEEE Transactions on Multimedia, 2014,16(5): 1268-1281.
|
[4] |
BAKER S, KANADE T. Limits on super-resolutionand how to break them [J]. IEEE Transactions on PatternAnalysis and Machine Intelligence, 2002, 24(9):1167-1183.
|
[5] |
WANG X Y, TANG X O. Hallucinating face by eigentransformation [J]. IEEE Transactions on Systems,Man, and Cybernetics, Part C, 2005, 35(3): 425-434.
|
[6] |
HU Y, LAM K M, SHEN T Z, et al. A novel kernelbasedframework for facial-image hallucination [J]. Imageand Vision Computing, 2011, 29: 219-229.
|
[7] |
HUANG H, HE H T, FAN X, et al. Super-resolution ofhuman face image using canonical correlation analysis[J]. Pattern Recognition, 2010, 43: 2532-2543.
|
[8] |
AN L, BHANU B. Face image super-resolution using2D CCA [J]. Signal Processing, 2014, 103: 184-194.
|
[9] |
TANG S Z, XIAO L, LIU P F, et al. Partial leastsquaresregression on common feature space for singleimage superresolution [J]. Journal of Electronic Imaging,2014, 23(5): 053006.
|
[10] |
ROWEIS S T, SAUL L K. Nonlinear dimensionality reductionby locally linear embedding [J]. Science, 2000,290(5500): 2323-2326.
|
[11] |
CHANG H, YEUNG D Y, XIONG Y M. Superresolutionthrough neighbor embedding [C]//Proceedingsof 17th IEEE Computer Society Conferenceon Computer Vision and Pattern Recognition. Washington,USA: IEEE, 2004: 1275-1282.
|
[12] |
ZHU Q D, SUN L, CAI C T. Non-local neighbor embeddingfor image super-resolution through FoE features[J]. Neurocomputing, 2014, 141: 211-222.
|
[13] |
CHEN X X, QI C. Low-rank neighbor embedding forsingle image super-resolution [J]. IEEE Signal ProcessingLetters, 2014, 21(1): 79-82.
|
[14] |
JIANG J J, HU R M, WANG Z Y, et al. Facial imagehallucination through coupled-layer neighbor embedding[J]. IEEE Transactions on Circuits and Systemsfor Video Technology, 2015, 26(9): 1674-1684.
|
[15] |
YANG J C, WRIGHT J, HUANG T S, et al. Imagesuper-resolution via sparse representation [J]. IEEETransactions on Image Processing, 2010, 19(11): 2861-2873.
|
[16] |
MA X, ZHANG J P, QI C. Hallucinating face byposition-patch [J]. Pattern Recognition, 2010, 43:2224-2236.
|
[17] |
JUNG C, JIAO L C, LIU B, et al. Position-patch basedface hallucination using convex optimization [J]. IEEESignal Processing Letters, 2011, 18(6): 367-370.
|
[18] |
JIANG J J, CHEN C, MA J Y, et al. SRLSP: A faceimage super-resolution algorithm using smooth regressionwith local structure prior [J]. IEEE Transactionson Multimedia, 2016, 19(1): 27-40.
|
[19] |
DONG C, LOY C C, HE K M, et al. Image superresolutionusing deep convolutional networks [J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2016, 38(2): 295-307.
|
[20] |
GAO G W, YANG J, LAI Z H, et al. Nuclear normregularized coding with local position-patch and nonlocalsimilarity for face hallucination [J]. Digital SignalProcessing, 2017, 64: 107-120.
|
[21] |
CAI J F, CAND`ES E J, SHEN Z W. A singularvalue thresholding algorithm for matrix completion [J].SIAM Journal on Optimization, 2010, 20(4): 1956-1982.
|
[22] |
GU S H, ZHANG L, ZUO W M, et al. Weightednuclear norm minimization with application to imagedenoising [C]//Proceedings of IEEE Conference onComputer Vision and Pattern Recognition. Columbus,USA: IEEE, 2014: 2862-2869.
|
[23] |
YANG J, LUO L, QIAN J J, et al. Nuclear norm basedmatrix regression with applications to face recognitionwith occlusion and illumination changes [J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2016, 39(1): 156-171.
|
[24] |
LIN Z C, CHEN M M, MA Y. The augmentedLagrange multiplier method for exact recovery ofcorrupted low-rank matrices [EB/OL]. (2010-09-26)[2017-12-17]. https://arxiv.org/abs/1009.5055.
|
[25] |
HANSSON A, LIU Z, VANDENBERGHE L. Subspacesystem identification via weighted nuclear norm optimization[C]//Proceedings of 51st IEEE Conference onDecision and Control. Hawaii, USA: IEEE, 2012: 3439-3444.
|
[26] |
BECK A, TEBOULLE M. A fast iterative shrinkagethresholdingalgorithm for linear inverse problems [J].SIAM Journal on Imaging Sciences, 2009, 2(1): 183-202.
|
[27] |
GU S H, XIE Q, MENG D Y, et al. Weighted nuclearnorm minimization and its applications to low levelvision [J]. International Journal of Computer Vision,2017, 121(2): 183-208.
|
[28] |
THOMAZ C E, GIRALDI G A. A new ranking methodfor principal components analysis and its applicationto face image analysis [J]. Image and Vision Computing,2010, 28: 902-913.
|
[29] |
WANG Z, BOVIK A C, SHEIKH H R, et al. Imagequality assessment: From error visibility to structuralsimilarity [J]. IEEE Transactions on Image Processing,2004, 13(4): 600-612.
|
[30] |
QU SM, HU R M, CHEN S H, et al. Robust face superresolutionvia position-patch neighborhood preserving[C]//Proceedings of 15th IEEE International Conferenceon Multimedia and Expo Workshops (ICMEW).Chengdu, China: IEEE, 2014: 1-5.
|
[31] |
XIE Y, QU Y Y, TAO D C, et al. Hyperspectral imagerestoration via iteratively regularized weighted Schattenp-norm minimization [J]. IEEE Transactions onGeoscience and Remote Sensing, 2016, 54(8): 4642-4659.
|