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Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method

Yıl 2010, Cilt: 23 Sayı: 2, 35 - 51, 31.12.2010

Kemal Özkan Erol Seke

Öz


Registration errors are well-known problems in super-resolution restoration applications. Local outliers are caused by the registration errors and objects in motion. Instead of blind rejection of local outliers, we favor for the detected edges. For that, pre-estimated high-resolution image is searched for some specified edge and corner patterns. Outlier rejection is performed based on the pattern found. The method is shown to reduce over-blurring caused by the regularization that is common in iterative super-resolution restoration algorithms.

Anahtar Kelimeler

Super-resolution outlier removal

Kaynakça

  • [1] B. C. Tom and A. K. Katsaggelos, “Reconstruction of a High-Resolution Image by Simultaneous Registration, Restoration, and Interpolation of Low-Resolution Images,” Proc. 1995 IEEE International Conf. on Image Processing, pp. II-539-542, Oct. 1995, Washington, DC.
  • [2] H.S. Hou, H.C. Andrews, "Cubic splines for image interpolation and digital filtering," IEEE Transactions on Acoustics, Speech, Signal Processing ASSP-26, Vol.6, pp. 508–517, 1978.
  • [3] R. Y. Tsai and T. S. Huang. “Multiframe image restoration and registration,” In R. Y. Tsai and T. S. Huang, editors, Advances in Computer Vision and Image Processing, Vol.1, pp. 317–339. JAI Press Inc., 1984.
  • [4] M. Irani and S. Peleg, “Improving Resolution by Image Registration,” Computer Vision, Graphics and Image Processing, vol.53, pp. 231–239, May 1991.
  • [5] K.P. Hong, J.K. Paik, H. Ju Kim, C. Ho Lee, “An edge-preserving image interpolation system for a digital camcorder”, IEEE Transactions on Consumer Electronics, Vol.42, No.3, 1996.
  • [6] Battiato S., Gallo G., Stanco F., “A Locally-Adaptive Zooming Algorithm for Digital Images”, Elsevier Image Vision and Computing Journal, Vol.20, No.11, pp.805-812, 2002.
  • [7] C. Bauman, K. Sauer, “A Generalized Gaussian Image Model for Edge-Preserving MAP Estimation”, IEEE Transactions on Image Processing, Vol.2, No.3, pp.296-310, July 1993.
  • [8] S. Tebaul, L. Blanc-Féraud, G. Aubert, M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE's”, IEEE Transactions on Image Processing, Vol.7, No.3, pp.387-397, 1998.
  • [9] M. Belge, M.E. Kilmer, E.L. Miller, “Wavelet domain image restoration with adaptive edge-preserving regularization”, IEEE Transactions on Image Processing, Vol.9, No.4, pp.597-608, 2000.
  • [10] M.K. Ng, N.K. Boze, “Mathematical Analysis of Super-Resolution Methodology”, IEEE Signal Processing Magazine, Vol.20, No.3, pp. 62-74, 2003.
  • [11] R. Pan, S.J. Reeves, “Efficient huber-markov edge-preserving image restoration”, IEEE Transactions on Image Processing, Vol.5, No.2, pp.3728-3735, 2006.
  • [12] R. K. Ward, “Restorations of Differently Blurred Versions of an Image with Measurement Errors in the PSF’s”, IEEE Transactions on Image Processing, Vol.2, No.3, pp.369-381, 1993.
  • [13] M. Elad and A. Feuer, “Restoration of a Single Superresolution Image from Several Blurred, Noisy and Undersampled Measured Images”, IEEE Transactions on Image Processing, Vol.6, No.12, pp.1646-1658, 1997.
  • [14] M. K. Özkan, A. M. Tekalp and M. I. Sezan, “POCS Based Restoration of Space-Varying Blurred Images” IEEE Transactions on Image Processing, Vol.2, No.4, pp.450-454, 1994.
  • [15] A. Patti, M. I. Sezan and A. M. Tekalp, “Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzero Aperture Time”, IEEE Trans. Image Processing, Vol.6, No.8, pp.1064-1076, 1997.
  • [16] S. Borman, R. L. Stevenson, “Spatial Resolution Enhancement of Low-Resolution Image Sequences. A comprehensive Review With Directions for Future Research”, Tech. Rep., Laboratory for Image and Signal Analysis, University of Notre Dame, 1998.
  • [17] S. C. Park, M. K. Park and M. G. Kang, “Super-Resolution Image Reconstruction: A Technical Overview” IEEE Signal Processing Magazine, pp. 21-36, 2003.
  • [18] S. Baker and T. Kanade, “Limits on Super-Resolution and How to Break Them”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.24, No.9, pp.1167-1183, 2002.
  • [19] Z. Lin and H. Shum, “Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.1, pp.83-97, 2004.
  • [20] E. Seke, K. Özkan, “Least Squares Sub-pixel Registration Refinement Using Area Sampler Model”, Journal of Mathematical Imaging and Vision, Vol.26, No.1-2, pp.19-25, 2006
  • [21] M. Trimeche and J. Yrjänäinen, “A Method for Simultaneous Outlier Rejection in Image Super-Resolution”, Lecture Notes in Computer Science, Vol.2849, No.2003, pp. 188-195, Springer Berlin.
  • [22] M. Trimeche, R. C. Bilcu and J. Yrjänäinen, “Adaptive Outlier Rejection in Image Super-resolution”, EURASIP Journal on Applied Signal Processing, pp.1-12, 2006. Edge-Preserving Super-Resolution Using An Adaptive Outlier Rejection Method 51
  • [23] A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super resolution”, in Proc. Int. Conf. Computer Vision and Patern Recognition, vol. 1, pp. 645–650, 2001.
  • [24] S. Farsiu, M. D. Robinson, M. Elad and P. Milanfar, “Fast and Robust Multiframe Super Resolution,” IEEE Transactions on Image Processing, Vol.13, No.10, pp.1327-1344, 2004.
  • [25] Farsiu, S., D. Robinson, M. Elad, P. Milanfar, “Robust Shift-and-Add Approach to Super-resolution”, Proceedings of the SPIE Annual Meeting, San Diego, CA, 2003.
  • [26] Barber, C. B., D.P. Dobkin, and H.T. Huhdanpaa, “The Quickhull Algorithm for Convex Hulls”, ACM Transactions on Mathematical Software, Vol.22, No.4, pp.469-483, 1996.
  • [27] M. C. Chiang and T. E. Boulte, “Efficient super-resolution via image warping”, Image and Vision Computing, Vol.18, No.10, pp.761–771, 2000.
  • [28] S. Farsiu, “MDSP Resolution Enhancement Software – User's Manual”, University of California, Santa Cruz, 2004. available online: http://www.soe.ucsc.edu/~milanfar/software/superresolution.html

Yıl 2010, Cilt: 23 Sayı: 2, 35 - 51, 31.12.2010

Kemal Özkan Erol Seke

Öz

Kaynakça

  • [1] B. C. Tom and A. K. Katsaggelos, “Reconstruction of a High-Resolution Image by Simultaneous Registration, Restoration, and Interpolation of Low-Resolution Images,” Proc. 1995 IEEE International Conf. on Image Processing, pp. II-539-542, Oct. 1995, Washington, DC.
  • [2] H.S. Hou, H.C. Andrews, "Cubic splines for image interpolation and digital filtering," IEEE Transactions on Acoustics, Speech, Signal Processing ASSP-26, Vol.6, pp. 508–517, 1978.
  • [3] R. Y. Tsai and T. S. Huang. “Multiframe image restoration and registration,” In R. Y. Tsai and T. S. Huang, editors, Advances in Computer Vision and Image Processing, Vol.1, pp. 317–339. JAI Press Inc., 1984.
  • [4] M. Irani and S. Peleg, “Improving Resolution by Image Registration,” Computer Vision, Graphics and Image Processing, vol.53, pp. 231–239, May 1991.
  • [5] K.P. Hong, J.K. Paik, H. Ju Kim, C. Ho Lee, “An edge-preserving image interpolation system for a digital camcorder”, IEEE Transactions on Consumer Electronics, Vol.42, No.3, 1996.
  • [6] Battiato S., Gallo G., Stanco F., “A Locally-Adaptive Zooming Algorithm for Digital Images”, Elsevier Image Vision and Computing Journal, Vol.20, No.11, pp.805-812, 2002.
  • [7] C. Bauman, K. Sauer, “A Generalized Gaussian Image Model for Edge-Preserving MAP Estimation”, IEEE Transactions on Image Processing, Vol.2, No.3, pp.296-310, July 1993.
  • [8] S. Tebaul, L. Blanc-Féraud, G. Aubert, M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE's”, IEEE Transactions on Image Processing, Vol.7, No.3, pp.387-397, 1998.
  • [9] M. Belge, M.E. Kilmer, E.L. Miller, “Wavelet domain image restoration with adaptive edge-preserving regularization”, IEEE Transactions on Image Processing, Vol.9, No.4, pp.597-608, 2000.
  • [10] M.K. Ng, N.K. Boze, “Mathematical Analysis of Super-Resolution Methodology”, IEEE Signal Processing Magazine, Vol.20, No.3, pp. 62-74, 2003.
  • [11] R. Pan, S.J. Reeves, “Efficient huber-markov edge-preserving image restoration”, IEEE Transactions on Image Processing, Vol.5, No.2, pp.3728-3735, 2006.
  • [12] R. K. Ward, “Restorations of Differently Blurred Versions of an Image with Measurement Errors in the PSF’s”, IEEE Transactions on Image Processing, Vol.2, No.3, pp.369-381, 1993.
  • [13] M. Elad and A. Feuer, “Restoration of a Single Superresolution Image from Several Blurred, Noisy and Undersampled Measured Images”, IEEE Transactions on Image Processing, Vol.6, No.12, pp.1646-1658, 1997.
  • [14] M. K. Özkan, A. M. Tekalp and M. I. Sezan, “POCS Based Restoration of Space-Varying Blurred Images” IEEE Transactions on Image Processing, Vol.2, No.4, pp.450-454, 1994.
  • [15] A. Patti, M. I. Sezan and A. M. Tekalp, “Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzero Aperture Time”, IEEE Trans. Image Processing, Vol.6, No.8, pp.1064-1076, 1997.
  • [16] S. Borman, R. L. Stevenson, “Spatial Resolution Enhancement of Low-Resolution Image Sequences. A comprehensive Review With Directions for Future Research”, Tech. Rep., Laboratory for Image and Signal Analysis, University of Notre Dame, 1998.
  • [17] S. C. Park, M. K. Park and M. G. Kang, “Super-Resolution Image Reconstruction: A Technical Overview” IEEE Signal Processing Magazine, pp. 21-36, 2003.
  • [18] S. Baker and T. Kanade, “Limits on Super-Resolution and How to Break Them”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.24, No.9, pp.1167-1183, 2002.
  • [19] Z. Lin and H. Shum, “Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.1, pp.83-97, 2004.
  • [20] E. Seke, K. Özkan, “Least Squares Sub-pixel Registration Refinement Using Area Sampler Model”, Journal of Mathematical Imaging and Vision, Vol.26, No.1-2, pp.19-25, 2006
  • [21] M. Trimeche and J. Yrjänäinen, “A Method for Simultaneous Outlier Rejection in Image Super-Resolution”, Lecture Notes in Computer Science, Vol.2849, No.2003, pp. 188-195, Springer Berlin.
  • [22] M. Trimeche, R. C. Bilcu and J. Yrjänäinen, “Adaptive Outlier Rejection in Image Super-resolution”, EURASIP Journal on Applied Signal Processing, pp.1-12, 2006. Edge-Preserving Super-Resolution Using An Adaptive Outlier Rejection Method 51
  • [23] A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super resolution”, in Proc. Int. Conf. Computer Vision and Patern Recognition, vol. 1, pp. 645–650, 2001.
  • [24] S. Farsiu, M. D. Robinson, M. Elad and P. Milanfar, “Fast and Robust Multiframe Super Resolution,” IEEE Transactions on Image Processing, Vol.13, No.10, pp.1327-1344, 2004.
  • [25] Farsiu, S., D. Robinson, M. Elad, P. Milanfar, “Robust Shift-and-Add Approach to Super-resolution”, Proceedings of the SPIE Annual Meeting, San Diego, CA, 2003.
  • [26] Barber, C. B., D.P. Dobkin, and H.T. Huhdanpaa, “The Quickhull Algorithm for Convex Hulls”, ACM Transactions on Mathematical Software, Vol.22, No.4, pp.469-483, 1996.
  • [27] M. C. Chiang and T. E. Boulte, “Efficient super-resolution via image warping”, Image and Vision Computing, Vol.18, No.10, pp.761–771, 2000.
  • [28] S. Farsiu, “MDSP Resolution Enhancement Software – User's Manual”, University of California, Santa Cruz, 2004. available online: http://www.soe.ucsc.edu/~milanfar/software/superresolution.html
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Konular Bilgisayar Yazılımı
Bölüm Araştırma Makaleleri
Yazarlar

Kemal Özkan

Erol Seke

Yayımlanma Tarihi 31 Aralık 2010
Kabul Tarihi 23 Temmuz 2010
Yayımlandığı Sayı Yıl 2010 Cilt: 23 Sayı: 2

Kaynak Göster

APA Özkan, K., & Seke, E. (2010). Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 23(2), 35-51.
AMA Özkan K, Seke E. Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method. ESOGÜ Müh Mim Fak Derg. Aralık 2010;23(2):35-51.
Chicago Özkan, Kemal, ve Erol Seke. “Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 23, sy. 2 (Aralık 2010): 35-51.
EndNote Özkan K, Seke E (01 Aralık 2010) Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 23 2 35–51.
IEEE K. Özkan ve E. Seke, “Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method”, ESOGÜ Müh Mim Fak Derg, c. 23, sy. 2, ss. 35–51, 2010.
ISNAD Özkan, Kemal - Seke, Erol. “Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 23/2 (Aralık 2010), 35-51.
JAMA Özkan K, Seke E. Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method. ESOGÜ Müh Mim Fak Derg. 2010;23:35–51.
MLA Özkan, Kemal ve Erol Seke. “Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, c. 23, sy. 2, 2010, ss. 35-51.
Vancouver Özkan K, Seke E. Edge-Preserving Super-Resolutıon Using An Adaptive Outlier Rejection Method. ESOGÜ Müh Mim Fak Derg. 2010;23(2):35-51.
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