Stark H, Oskoui P. High resolution image recovery from image-plane arrays, using convex projections. Journal of the Optical Society of America A, 1989, 6(11): 1715-1726
[2]
Kim S P, Bose N K, Valenzuela H M. Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990, 38(6): 1013-1027
[3]
Schultz R R, Stevenson R L. Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing, 1996, 5(6): 996-1011
[4]
Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[5]
Guo Li, Liao Yu, Chen Wei-Long. An improved registration algorithm on video super resolution reconstruction. Journal of Hubei University for Nationalities (Natural Science Edition), 2010, 28(2): 177-180, 183(郭黎, 廖宇, 陈为龙. 基于改进SIFT的视频超分辨率重建快速配准算法研究. 湖北民族学院学报(自然科学版), 2010, 28(2): 177-180, 183
[6]
Amintoosi M, Fathy M, Mozayani N. Regional varying image super-resolution. In: Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization. Sanya, China: IEEE, 2009. 913-917
[7]
Amintoosi M, Fathy M, Mozayani N. A fast image registration approach based on SIFT key-points applied to super-resolution. The Imaging Science Journal, 2012, 60(4): 185-201
[8]
Biswas S, Aggarwal G, Flynn P J. Pose-robust recognition of low-resolution face images. In: Proceedings of the 2011 International Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI: IEEE, 2011. 601-608
[9]
Ferreira R U, Hung E M, De Queiroz R L. Video super-resolution based on local invariant features matching [Online], available: http://icip2012.com/AcceptedPaperList. asp, October 12, 2012
[10]
The Ecole Polytechnique Fédéral de Lausanne. Super-Resolution application (version 2.0) [Online], available: http://lcavwww.epfl.ch/software/superresolution, May 9, 2007
[11]
Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of Association for Computing Machinery, 1981, 24(6): 381-395
[12]
Cai Tao, Duan Shan-Xu, Li De-Hua. Performance comparison and analysis of fundamental matrix estimating methods for computer vision applications. Computer Science, 2009, 36(1): 243-247, 289 (蔡涛, 段善旭, 李德华. 视觉基础矩阵估计方法的性能比较与分析. 计算机科学, 2009, 36(1): 243-247, 289
[13]
Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600-612
[14]
Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine, 2003, 20(3): 21-36
[15]
Zomet A, Rav-Acha A, Peleg S. Robust super-resolution. In: Proceedings of the 2001 International Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii, USA: IEEE, 2001. 645-650
[16]
Pham T Q, van Vliet L J, Schutte K. Robust fusion of irregularly sampled data using adaptive normalized convolution. Journal on Applied Signal Processing, 2006, 2006: 1-12
[17]
Seong Y M, Park H. A high-resolution image reconstruction method from low-resolution image sequence. In: Proceedings of the 16th International Conference on Image Processing (ICIP). Cairo, Egypt: IEEE, 2009. 1181-1184
[18]
Nemra A, Aouf N. Robust invariant automatic image mosaicing and super resolution for UAV mapping. In: Proceedings of the 6th International Symposium on Mechatronics and Its Applications (ISMA). Sharjah, UAE: IEEE, 2009. 1-7
[19]
Nasir H, Stankovic V, Marshall S. Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus. In: Proceedings of the 18th European Signal Processing Conference (EUSIPCO). Aalborg, Denmark: IEEE, 2010. 299-303
[20]
Su H, Wu Y, Zhou J. Super-resolution without dense flow. IEEE Transactions on Image Processing, 2012, 21(4): 1782-1795
[21]
Lowe D G. SIFT demo program (version 4) [Online], available: http://www.cs.ubc.ca/lowe/keypoints/, July 1, 2005
[22]
Pestak T C. Development of an Efficient Super-Resolution Image Reconstruction Algorithm for Implementation on a Hardware Platform [Master dissertation], Wright State University, Dayton, USA, 2010