Citation: | “A KPLS-Eigentransformation Model Based Face Hallucination Algorithm,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 683-686, 2012, |
S.Y. Wang, Z. Li, X.G. Li, “A panchromatic image-based spectralimagery super resolution algorithm”, Chinese Journal ofElectronics, Vol.20, No.4, pp.617-620, 2011.
|
S. Baker, T. Kanade, “Limits on super-resolution and how tobreak them”, IEEE Transactions on Pattern Analysis and MachineIntelligence, Vol.2, No.2, pp.1167-1183, 2002.
|
X.Wang, X. Tang, “Hallucinating face by eigentransformation”,IEEE Transactions on Systems, Man and Cybernetics, Part C:Applications and Reviews, Vol.35, No.3, pp.425-434, 2005.
|
W. Wu, Z. Liu, X. He, “Learning-based super resolution usingkernel partial least squares”, Proceedings of Image VisionCompute, Vol.29, No.6, pp.394-406, 2011.
|
R. Roman, K. Nicole, “Overview and recent advances in partialleast squares”, Lecture Notes in Computer Science, Vol.3940,pp.34-51, 2006.
|
R. Rosipal, “Kernel partial least squares for nonlinear regressionand discrimination” Neural Network World, Vol.13, No.3,pp.291-300, 2003.
|
G. Wen, B. Cao, S. Shan, X. Chen, D. Zhou, X. Zhang, D. Zhao,“The CAS-PEAL large-scale Chinese face database and baselineevaluations”, IEEE Transactions on Systems, Man and Cybernetics,Part A: Systems and Humans, Vol.38, No.1, pp.149-161,2008.
|
P.J. Phillips, H. Moon, P.J. Rauss and S. Rizvi, “The FERETevaluation methodology for face recognition algorithms”, IEEETransactions on Pattern Analysis and Machine Intelligence,Vol.22, No.10, pp.1090-1103, 2000.
|
Z.Wang, A.C. Bovik and L.G. Lu, “Why is image quality assessmentso difficult?”, Speech and Signal Processing, USA, Vol.8,pp.3313-3316, 2002.
|