|
[1] Kim, S. P., Bose, N. K., and Valenzuela, H. M., "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Transactions Acoustic, Speech, Signal Processing, Vol. 38, Issue 6, pp. 1013-1027, 1990. [2] Gunturk, B. K., Batur, A. U., Altunbasak, Y., Hayes, M. H., and Mersereau, R. M., "Eigenface-domain super-resolution for face recognition," IEEE Transactions on Image Processing, Vol. 12, No.5, pp. 597-606, 2003. [3] Baker, S. and Kanade, T., "Limits on super-resolution and how to break them," IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 1167-1183, 2002. [4] Lindley, C. A., Practical image processing in C, Wiley-Interscience, USA, pp. 23-25, 1994. [5] Valenzuela, H. M., "A two-dimensional recursive model for bilinear systems with applications to image reconstruction," IEEE Transactions on Circuits and System, Vol. 37, No. 3, pp. 354-363, 1990. [6] Keys, R. G., "Cubic convolution interpolation for digital image processing," IEEE Transactions Acoustic, Speech and Signal Processing, Vol. 29, Issue 6, pp. 1153-1160, 1981. [7] Clark, J. J., Palmer, M. R., and Laurence, P. D., "A transformation method for the reconstruction of functions from nonuniformly spaced samples," IEEE Transactions Acoustic, Speech and Signal Processing, Vol. 33, Issue 5, pp. 1151-1165, 1985. [8] Park, S. C., Park, M. K., and Kang, M. G., "Super-resolution image reconstruction: a technical overview," IEEE Signal Processing Magazine, Vol. 20, Issue 3, pp. 21-36, 2003. [9] Stark, H. and Oskoui, P., "High resolution image recovery from image-plane arrays, using convex projections," J. Opt. Soc. Am. A, Vol. 6, No. 11, pp. 1715-1726, 1989. [10] Irani, M. and Peleg, S., "Improving resolution by image registration," CVGIP: Graphical Models and Image Processing, Vol. 53, Issue 3, pp. 231-239, 1991. [11] Tipping, M. E. and Bishop, C. M., "Bayesian image super-resolution," Proceedings, International Neural Information Processing Systems Conference, Vancouver, Canada, pp. 1279-1286, 2002. [12] Kanemura, A., Maeda, S., and Ishii, S., "Edge-preserving bayesian image superresolution based on compound markov random fields," Proceedings, International Artificial Neural Networks Conference, San Sebastian, Spain, pp. 611-620, 2007. [13] Zhuang, Y., Zhang, J., and Wu, F., " Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation," Pattern Recognition, Vol. 40, No. 11, pp. 3178-3194, 2007. [14] Capel, D. and Zisserman, A., "Super-resolution from multiple views using learnt image models," Proceedings, International Computer Vision and Pattern Recognition Conference, Hawaii, USA, pp. 627-634, 2001. [15] Chakrabarti, A., Rajagopalan, A. N., and Chellappa, R., "Super-resolution of face images using kernel PCA-based prior," IEEE Transactions on Multimedia, Vol. 38, Issue 4, pp. 888-892, 2007. [16] Chang, H., Yeung, D., and Xiong, Y., "Super-resolution through neighbor embedding," Proceedings, International Computer Vision and Pattern Recognition Conference, Washington, USA, pp. 275-282, 2004. [17] Jain, A. K., Flynn, P., and Ross, A. A., Handbook of Biometrics, Springer-Verlag, USA, pp. 62-64, 2007. [18] Wang, Y. K. and Huang, C. R., "Face image super-resolution using two-dimensional locality preserving projection," Proceedings, International Intelligent Information Hiding and Multimedia Signal Processing Conference, Kyoto, Japna, 2009 (Accepted). [19] Kumar, B. G. V. and Aravind, R., "A 2D model for face superresolution," Proceedings, International Pattern Recognition Conference, Florida, USA, pp.1-4, 2008. [20] Chen, S., Zhao, H., Kong, M., and Luo, B., "2D-LPP: a two-dimensional extension of locality preserving projects," Neurocomputing, Vol. 70, Issue 4-6, pp. 912-921, 2007. [21] Niu, B., Yang, Q., Shiu, S. C. K., and Pal, S. K., "Two-dimensional Laplacianfaces method for face recognition," Pattern Recognition, Vol. 41, Issue 10, pp. 3237-3243, 2008. [22] Turk, M. A. and Pentland, A. P., "Face recognition using eigenfaces," Proceedings, International Computer Vision and Pattern Recognition Conference, Maui, USA, pp. 586-591, 1991. [23] Yang, J., Zhang, D., Frangi, A. F., and Yang, J., "Two-dimensional PCA: a new approach to appearance-based face representation and recognition," IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 26, Issue 1, pp. 131-137, 2004. [24] Zhang, D. and Zhou, Z., "(2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition," Neurocomputing, Vol. 69, Issue 1-3, pp. 224-231, 2005. [25] Roweis, S. T. and Saul, L. K., "Nonlinear dimensionality reduction by locally linear embedding," Science, Vol. 290, No. 5500, pp. 2323-2326, 2000. [26] Tenenbaum, J. B., Silva, V. D., and Langford, J. C., "A global geometric framework for nonlinear dimeensionality reduction," Science, Vol. 290, No. 11, pp. 2319-2323, 2000. [27] Belkin, M. and Niyogi, P., "Laplacian eigenmaps and spectral techniques for embedding and clustering," Proceedings, International Neural Information Processing System Conference, Cambridge, USA, pp. 585-591, 2002. [28] Donoho, D. L. and Grimes, C., "Hessian eigenmaps: new locally linear embedding techniques for high dimensional data," Proceedings, International National Academy of Sciences Conference, Washington, USA, pp. 5591-5596, 2003. [29] Bengio, Y., Paiement, J. F., and Vincent, P. “Out-of-sample extensions for LLE, ISOMAP, MDS, eigenmaps and spectral clustering,” Proceedings, International Neural Information Processing Systems Conference, Vancouver, Canada, pp. 177-184, 2003. [30] He, X. and Niyogi, P., "Locality preserving projections," Proceedings, International Neural Information Processing Systems Conference, Vancouver, Canada, pp. 585-591, 2003.
|