A robust variational approach to super-resolution with nonlocal TV regularisation term

REFERENCES

• Park SC, Park MK, Kang MG. Super-resolution image reconstruction: a technical review. IEEE Signal Process. Mag., 2003, 20, 21–36.
   Web of Science ®Google Scholar
• Farsiu S, Robinson D, Elad M, Milanfar P. Advances and challenges in super-resolution. Int. J. Imaging Syst. Technol., 2004, 14, 47–57.
   Web of Science ®Google Scholar
• van Ouwerkerk JD. Image super resolution survey. Image Vis. Comput., 2006, 24, 1039–1052.
   Web of Science ®Google Scholar
• Tian J, Ma KK. A survey on super resolution imaging. Signal Image Video Process., 2011, 5, 329–342.
   Web of Science ®Google Scholar
• Tsai R, Huang. T. Multi-frame image restoration and registration. Adv. Comput. Vis. Image Process., 1984, 1, 317–339.
   Google Scholar
• Kim SP, Bose NK, Valenzuela HM. Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Trans. Acoust. Speech Signal Process., 1990, 38, 1013–1027.
   Google Scholar
• Kim SP, Su WY. Recursive high-resolution reconstruction of blurred multiframe images. IEEE Trans. Image Process., 1993, 2, 534–539.
   PubMed Web of Science ®Google Scholar
• Rhee SH, Kang MG. Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Opt. Eng., 1999, 38, 1348–1356.
   Web of Science ®Google Scholar
• Chappalli MB, Bose NK. Simultaneous noise filtering and super resolution with second generation wavelets. IEEE Signal Process. Lett., 2005, 12, 772–775.
   Web of Science ®Google Scholar
• Irani M, Peleg S. Motion analysis for image enhancement resolution, occlusion, and transparency. J. Visual Commun. Image Represent., 1993, 4, 324–335.
   Google Scholar
• Irani M, Peleg S. Improving resolution by image registration. CVGIP: Graph. Models Image Process., 1991, 53, 231–239.
   Google Scholar
• Patti AJ, Altunbask Y. Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants. IEEE Trans. Image Process., 2001, 10, 179–186
   PubMed Web of Science ®Google Scholar
• Aguena MLS, Mascarenhas NDA. Multispectral image data fusion using POCS and super-resolution. Comput. Vis. Image Understand., 2006, 102, 178–187.
   Web of Science ®Google Scholar
• Schultz R, Stevenson R. Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process., 1996, 5, 996–1011.
   PubMed Web of Science ®Google Scholar
• Shen H, Zhang L, Huang B. A MAP approach for joint motion estimation segmentation and super resolution. IEEE Trans. Image Process., 1997, 6, 1646–1658.
   PubMedGoogle Scholar
• He Y, Yap KH, Che L, Chau LP. A soft MAP frame for blind super-resolution image reconstruction. Image Vis. Comput., 2009, 27, 364–373.
   Web of Science ®Google Scholar
• Freeman WT, Jones TR, Pasztor EC. Example-based super-resolution. IEEE Comput. Graph. Appl., 2002, 22, 56–65.
   Web of Science ®Google Scholar
• Joshi MV, Chaudhuri S, Panuganti R. A learning-based method for image super-resolution from zoomed observations. IEEE Trans. Syst., Man Cybern., 2005, 35, 527–537.
   PubMed Web of Science ®Google Scholar
• Ni K, Nguyen TQ. Image superresolution using support vector regression. IEEE Trans. Image Process., 2007, 16, 1596–1610.
   PubMed Web of Science ®Google Scholar
• Elad M, Feuer A. Restoration of a single super resolution image from several blurred, noisy, and undersampled measured images. IEEE Trans. Image Process., 1997, 6, 1646–1658.
   PubMed Web of Science ®Google Scholar
• Nguyen N, Milanfar P, Golub G. A computationally efficient super resolution image reconstruction algorithm. IEEE Trans. Image Process., 2001, 10, 573–583
   PubMed Web of Science ®Google Scholar
• Lee E, Kang MG. Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration. IEEE Trans. Image Process., 2003, 12, 826–837.
   PubMed Web of Science ®Google Scholar
• Bo X, Zeng W. Super resolution reconstruction for license plate images of moving vehicles. J. Southeast Univ., 2010, 26, 457–460.
   Google Scholar
• Chan TF, Ng MK, Yau AC, Yip AM. Super resolution image reconstruction using fast inpainting algorithms. Appl. Comput. Harmon. Anal., 2007, 23, 3–24
   Web of Science ®Google Scholar
• Marquina A, Osher SJ. Image super-resolution by TV regularization and Bregman iteration. J. Sci. Comput., 2008, 37, 367–382.
   Web of Science ®Google Scholar
• Farsiu S, Robinson D, Elad M, Milanfar P. Fast and robust multi-frame super-resolution. IEEE Trans. Image Process., 2004, 3, 1327–1344.
   Google Scholar
• Li X, Hu Y, Gao X, Tao D, Ning B. A multi-frame image super-resolution method. Signal Process., 2010, 90, 405–414.
   Web of Science ®Google Scholar
• Budades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one. Multiscale Model. Simul., 2005, 4, 490–530.
   Web of Science ®Google Scholar
• Tomasi C, Manduchi R. Bilateral filtering for gray and color images, Proc. Int. Conf. on Computer vision: ICCV’98, Washington, DC, USA, January 1998, IEEE Computer Society, Vol. 6, pp. 836–846.
   Google Scholar
• Elad M. On the bilateral filter and ways to improve it. IEEE Trans. Image Process., 2002, 11, 1141–1151.
   PubMed Web of Science ®Google Scholar
• Gilboa G, Osher S. Nonlocal linear image regularization and supervised segmentation. SIAM Multiscale Model. Simul., 2007, 6, 595–630.
   Web of Science ®Google Scholar
• Gilloa G, Osher S. Nonlocal operators with applications to image processing. SIAM Multiscale Model. Simul., 2008, 7, 1005–1028.
   Web of Science ®Google Scholar
• Irani M, Peleg S. Improving resolution by image registration. CVGIP: Graph. Models Image Process., 1991, 53, 231–239.
   Google Scholar
• Zitova B, Flusser J. Image registration methods: a survey. Image Vis. Comput., 2003, 21, 977–1000.
   Web of Science ®Google Scholar
• Kim TK, Im JH, Paik JK. Video object segmentation and its salient motion detection using adaptive background generation. Electron. Lett., 2009, 45, 542–543.
   Web of Science ®Google Scholar
• Zhang L, Zhang H, Shen H, Li P. A super resolution reconstruction algorithm for surveillance images. Signal Process., 2010, 90, 848–859.
   Web of Science ®Google Scholar
• Hagege R, Francos JM. Parametric estimation of affine transformations: an exact linear solution. J. Math. Imaging Vis., 2010, 37, 1–16.
   Web of Science ®Google Scholar
• Keren D, Peleg S, Brada R. Image sequence enhancement using sub-pixel displacements, Proc. IEEE Conf. on Computer vision and pattern recognition: CVPR’88, Ann Arbor, MI, USA, June 1988, IEEE Computer Society, pp. 742–746.
   Google Scholar
• Rudin L, Osher S, Tatemi E. Nonlinear total variation based noise removal algorithms. Physica D, 1992, 60, 259–268.
   Web of Science ®Google Scholar
• Barash D. A fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Trans. Patt. Anal. Mach. Intell., 2002, 24, 844–847.
   Web of Science ®Google Scholar
• Iaroslavsky LP. Digital Picture Processing, an Introuduction, 1985 (Springer-Verlag, Berlin).
   Google Scholar
• Buades A, Coll B, Morel JM. A review of image denoising algorithm with a new one. SIAM Multiscale Model. Simul., 2005, 4, 490–530.
   Web of Science ®Google Scholar
• Huynh-Thu Q, Ghanhari M. Scope of validity of PSNR in image/video quality assessment. Electron. Lett., 2008, 44, 800–801.
   Web of Science ®Google Scholar