Capel D, Zisserman A. Computer vision applied to super resolution. IEEE Signal Processing Magazine, 2003, 20(3): 75-86
[2]
Borman S, Stevenson R. Spatial Resolution Enhancement of Low-resolution Image Sequences: A Comprehensive Review with Directions for Future Research, Technical Report, Laboratory Image and Signal Analysis, University of Notre Dame, 1998
[3]
Park S C, Park M K, Kan M G. Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine, 2003, 20(3): 21-36
[4]
Katartzis A, Petrou M. Current trends in super-resolution image reconstruction. Image Fusion: Algorithms and Applications. New York: Academic Press, 2008
[5]
Tai Y W, Liu S C, Brown M S, Lin S. Super resolution using edge prior and single image detail synthesis. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA: IEEE, 2010. 2400-2407
[6]
Rhee S, Kang M. Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Optical Engineering, 1999, 38(8): 1348-1356
[7]
Nguyen N, Milanfar P. An efficient wavelet-based algorithm for image superresolution. In: Proceedings of the 2000 International Conference on Image Processing. Vancouver, BC, Canada: IEEE, 2000, 2: 351-354
[8]
Lertrattanapanich S, Bose N K. High resolution image formation from low resolution frames using delaunay triangulation. IEEE Transactions on Image Processing, 2002, 11(12): 1427-1441
[9]
Nasonov A V, Krylov A S. Fast super-resolution using weighted median filtering. In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR). Istanbul: IEEE, 2010. 2230-2233
[10]
Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections. Optical Society of America, Journal, A: Optics and Image Science, 1989, 6(11): 1715-1726
[11]
Patti A J, Sezan M I, Murat T A. Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Transactions on Image Processing, 1997, 6(8): 1064-1076
[12]
Patti A J, Altunbasak Y. Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants. IEEE Transactions on Image Processing, 2001, 10(1): 179-186
[13]
Yu J, Xiao C B, Su K N. A method of gibbs artifact reduction for pocs super-resolution image reconstruction. In: Proceedings of the 8th International Conference on Signal Processing. Beijing, China: IEEE, 2006, 2: 1-4
[14]
Babacan S D, Molina R, Katsaggelos A K. Total variation super resolution using a variational approach. In: Proceedings of the 15th IEEE International Conference on Image Processing (ICIP). San Diego, CA: IEEE, 2008. 641-644
[15]
Chantas G K, Galatsanos N P, Woods N A. Super-resolution based on fast registration and maximum a posteriori reconstruction. IEEE Transactions on Image Processing, 2007, 16(7): 1821-1830
[16]
Bose N K, Lertrattanapanich S, Koo J. Advances in superresolution using L-curve. In: Proceedings of the 2001 IEEE International Symposium on Circuits and Systems. Sydney, NSW: IEEE, 2001, 2: 433-436
[17]
Huang Li-Li, Xiao Liang, Wei Zhi-Hui, Zhang Jun. A fast decoupling algorithm for image super-resolution reconstruction of space-invariant system. Acta Automatica Sinica, 2010, 36(2): 229-236(黄丽丽, 肖亮, 韦志辉, 张军. 空间移不变系统图像超分辨复原的快速解耦算法. 自动化学报, 2010, 36(2): 229-236)
[18]
Baboulaz L, Dragotti P L. Exact feature extraction using finite rate of innovation principles with an application to image super-resolution. IEEE Transactions on Image Processing, 2009, 18(2): 281-298
[19]
Shen H F, Zhang L P, Huang B, Li P X. A map approach for joint motion estimation, segmentation, and super resolution. IEEE Transactions on Image Processing, 2007, 16(2): 479-490
[20]
Su H, Tang L, Wu Y, Tretter D, Zhou J. Spatially adaptive block-based super-resolution. IEEE Transactions on Image Processing, 2012, 21(3): 1031-1045
[21]
Takeda H, Milanfar P, Protter M, Elad M. Super-resolution without explicit subpixel motion estimation. IEEE Transactions on Image Processing, 2009, 18(9): 1958-1975
[22]
Protter M, Elad M, Takeda H, Milanfar P. Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Transactions on Image Processing, 2009, 18(1): 36-51
[23]
Kr？mer P, Hadar O, Benois-Pineau J, Domenger J P. Super-resolution mosaicing from mpeg compressed video. Signal Processing: Image Communication, 2007, 22(10): 845-865
[24]
Xu Z Q, Gan Z L, Zhu X C. Compressed video super-resolution reconstruction based on regularized algorithm. In: Proceedings of the 8th International Conference on Signal Processing. Beijing, China: IEEE, 2006, 2
[25]
Mudenagudi U, Banerjee B, Kalra P K. Space-time super-resolution using graph-cut optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 995-1008
[26]
Segall C A, Katsaggelos A K, Molina R, Mateos J. Bayesian resolution enhancement of compressed video. IEEE Transactions on Image Processing, 2004, 13(7): 898-911
[27]
Kong D, Han M, Xu W, Tao H, Gong Y H. A conditional random field model for video super-resolution. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR). Hong Kong, China: IEEE, 2006, 3: 619-622
[28]
Dai S Y, Han M, Xu W, Wu Y, Gong Y H. Soft edge smoothness prior for alpha channel super resolution. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Minneapolis, MN: IEEE, 2007. 1-8
[29]
Pentland A, Horowitz B. A practical approach to fractal-based image compression. In: Proceedings of the 1991 Data Compression Conference. Snowbird, UT: IEEE, 1991. 176- 185
[30]
Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Washington, DC, USA: IEEE, 2004, 1. I-275-I-282
[31]
Fan W, Yeung D Y. Image hallucination using neighbor embedding over visual primitive manifolds. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Minneapolis, MN: IEEE, 2007. 1-7
[32]
Adler A, Hel-Or Y, Elad M. A shrinkage learning approach for single image super-resolution with overcomplete representations. In: Proceedings of the 11th European Conference on Computer Vision (ECCV). 2010. Berlin, Heidelberg: Springer-Verlag, 622-635
[33]
Yang J C, Wright J, Huang T S, Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873
[34]
Wang J J, Zhu S H, Gong Y H. Resolution-invariant image representation and its applications. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009. Miami, FL: IEEE, 2009. 2512- 2519
[35]
Zhang W, Cham W K. Learning-based face hallucination in dct domain. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Anchorage, AK: IEEE, 2008. 1-8
[36]
Glasner D, Bagon S, Irani M. Super-resolution from a single image. In: Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. Kyoto: IEEE, 2009. 349- 356
[37]
Costa G H, Bermudez J C M. Are registration errors always bad for super-resolution? In: Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Honolulu, HI: IEEE, 2007, 1: I-569- I-572
[38]
Lin Z C, Shum H Y. Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 83-97
[39]
Robinson D, Milanfar P. Statistical performance analysis of super-resolution. IEEE Transactions on Image Processing, 2006, 15(6): 1413-1428
[40]
Damera-Venkata N, Kite T D, Geisler W S, Evans B L, Bovik A C. Image quality assessment based on a degradation model. IEEE Transactions on Image Processing, 2000, 9(4): 636-650
[41]
Sheikh H R, Bovik A C. Image information and visual quality. IEEE Transactions on Image Processing, 2006, 15(2): 430-444
[42]
Seshadrinathan K, Bovik A C. Motion tuned spatio-temporal quality assessment of natural videos. IEEE Transactions on Image Processing, 2010, 19(2): 335-350
[43]
Tsai R Y, Huang T S. Multiframe image restoration and registration. Advances in Computer Vision and Image Processing, 1984, 1: 317-339
[44]
Chaudhuri S. Super-Resolution Imaging. Boston: Kluwer Academic Publishers, 2001
[45]
Van Ouwerkerk J D. Image super-resolution survey. Image and Vision Computing, 2006, 24(10): 1039-1052
[46]
Sun J, Zhu J J, Tappen M F. Context-constrained hallucination for image super-resolution. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA: IEEE, 2010. 231-238
[47]
Farsiu S, Robinson M D, Elad M, Milanfar P. Fast and robust multiframe super resolution. IEEE Transactions on Image Processing, 2004, 13(10): 1327-1344
[48]
Katsaggelos A K, Lay K T, Galatsanos N P. A general framework for frequency domain multi-channel signal processing. IEEE Transactions on Image Processing, 1993, 2(3): 417-420
[49]
Ji H, Fermuller C. Robust wavelet-based super-resolution reconstruction: theory and algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(4): 649-660
[50]
Sanchez-Beato A, Pajares G. Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image. IEEE Transactions on Image Processing, 2008, 17(10): 1817-1826
[51]
Lin S C, Chen C T. Reconstructing vehicle license plate image from low resolution images using nonuniform interpolation method. International Journal of Image Processing, 2007, 1(2): 21
[52]
Banham M R, Katsaggelos A K. Digital image restoration. IEEE Signal Processing Magazine, 1997, 14(2): 24-41
[53]
Kim J Y, Park R H, Yang S. Super-resolution using pocs-based reconstruction with artifact reduction constraints. In: Proceedings of the 2005 Visual Communications and Image Processing, 5960, 2005. 59605B
[54]
Tom B, Katsaggelos A. Iterative algorithm for improving the resolution of video sequences. In: Proceedings of the 1996 SPIE, 2727, SPIE, 1996. 1430
[55]
Hennings-Yeomans P H, Baker S, Kumar B V K V. Simultaneous super-resolution and feature extraction for recognition of low-resolution faces. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Anchorage, AK: IEEE, 2008. 1-8
[56]
Chantas G, Galatsanos N, Likas A, Saunders M. Variational bayesian image restoration based on a product of t-distributions image prior. IEEE Transactions on Image Processing, 2008, 17(10): 1795-1805
[57]
Su H, Wu Y, Zhou J. Super-resolution without dense flow. IEEE Transactions on Image Processing, 2012, 21(4): 1782-1895
[58]
Yuan Q Q, Zhang L P, Shen H F, Li P X. Adaptive multiple-frame image super-resolution based on U-curve. IEEE Transactions on Image Processing, 2010, 19(12): 3157-3170
[59]
Wang Q, Tang X O, Shum H. Patch based blind image super resolution. In: Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV). Beijing, China: IEEE, 2005, 1: 709-716
[60]
Sun Yan-Yue, He Xiao-Hai, Song Hai-Ying, Chen Wei-Long. A block-matching image registration algorithm for video super-resolution reconstruction. Acta Automatica Sinica, 2011, 37(1): 37-43(孙琰玥, 何小海, 宋海英, 陈为龙. 一种用于视频超分辨率重建的块匹配图像配准方法. 自动化学报, 2011, 37(1): 37-43)
[61]
He Y, Yap K H, Chen L, Chau L P. A nonlinear least square technique for simultaneous image registration and super-resolution. IEEE Transactions on Image Processing, 2007, 16(11): 2830-2841
[62]
Takeda H, Farsiu S, Milanfar P. Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing, 2007, 16(2): 349-366
[63]
Protter M, Elad M. Super resolution with probabilistic motion estimation. IEEE Transactions on Image Processing, 2009, 18(8): 1899-1904
[64]
Gunturk B K, Altunbasak Y, Mersereau R M. Super-resolution reconstruction of compressed video using transform-domain statistics. IEEE Transactions on Image Processing, 2004, 13(1): 33-43
[65]
Xu Z Q, Zhu X C. Super-resolution reconstruction of compressed video based on adaptive quantization constraint set. In: Proceedings of the 1st International Conference on Innovative Computing, Information and Control. Beijing, China: IEEE, 2006, 1: 281-284
[66]
Begin I, Ferrie F P. Comparison of super-resolution algorithms using image quality measures. In: Proceedings of the 3rd Canadian Conference on Computer and Robot Vision. Washington, DC, USA: IEEE Computer Society, 2006. 72
[67]
Belekos S P, Galatsanos N P, Babacan S D, Katsaggelos A K. Maximum a posteriori super-resolution of compressed video using a new multichannel image prior. In: Proceedings of the 16th IEEE International Conference on Image Processing (ICIP). Cairo: IEEE, 2009. 2797-2800
[68]
Su H, Wu Y, Zhou J. Adaptive incremental video super-resolution with temporal consistency. In: Proceedings of the 18th IEEE International Conference on Image Processing (ICIP). Brussels, Belgium: IEEE, 2011. 1149-1152
[69]
Zibetti M V W, Mayer J. Simultaneous super-resolution for video sequences. In: Proceedings of the 2005 IEEE International Conference on Image Processing (ICIP). Genova: IEEE, 2005, 1: I-877
[70]
Mallat S, Yu G S. Super-resolution with sparse mixing estimators. IEEE Transactions on Image Processing, 2010, 19(11): 2889-2900
[71]
Freeman W T, Pasztor E C. Learning low-level vision. In: Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV). Kerkyra: IEEE, 1999, 2: 1182- 1189
[72]
Sun J, Zheng N N, Tao H, Shum H Y. Image hallucination with primal sketch priors. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Madison, WI, USA: IEEE, 2003, 2: II-729-36
[73]
Yang J C, Wright J, Huang T, Ma Y. Image super-resolution as sparse representation of raw image patches. In: Proceedings of the 2008 IEEE Conference of Computer Vision and Pattern Recognition (CVPR). Anchorage, AK: IEEE, 2008. 1-8
[74]
Kim K I, Kwon Y. Single-image super-resolution using sparse regression and natural image prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(6): 1127-1133
[75]
Wang J J, Zhu S H, Gong Y H. Resolution enhancement based on learning the sparse association of image patches. Pattern Recognition Letters, 2010, 31(1): 1-10
[76]
Liu C, Shum H Y, Freeman W T. Face hallucination: theory and practice. International Journal of Computer Vision, 2007, 75(1): 115-134
[77]
Hu Y, Lam K M, Qiu G P, Shen T Z. From local pixel structure to global image super-resolution: a new face hallucination framework. IEEE Transactions on Image Processing, 2011, 20(2): 433-445
[78]
Zhao W, Sawhney H S. Is super-resolution with optical flow feasible? In: Proceedings of the 7th European Conference on Computer Vision. London, UK: Springer-Verlag 2002. 599-613
[79]
Baker S, Kanade T. Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(9): 1167-1183
[80]
Tanaka M, Okutomi M. Theoretical analysis on reconstruc- tion-based super-resolution for an arbitrary PSF. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Diego, CA, USA: IEEE, 2005, 2: 947-954
[81]
Wang Z, Bovik A C, Lu L G. Why is image quality assessment so difficult? In: Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Orlando, FL, USA: IEEE, 2002, 4: IV-3313-IV-3316
[82]
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
[83]
Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 2006, 15(11): 3440-3451
