Robust maximum a posteriori image super-resolution

Abstract. A global robust M-estimation scheme for maximum a posteriori (MAP) image super-resolution which efficiently addresses the presence of outliers in the low-resolution images is proposed. In iterative MAP image super-resolution, the objective function to be minimized involves the highly resolved image, a parameter controlling the step size of the iterative algorithm, and a parameter weighing the data fidelity term with respect to the smoothness term. Apart from the robust estimation of the high-resolution image, the contribution of the proposed method is twofold: (1) the robust computation of the regularization parameters controlling the relative strength of the prior with respect to the data fidelity term and (2) the robust estimation of the optimal step size in the update of the high-resolution image. Experimental results demonstrate that integrating these estimations into a robust framework leads to significant improvement in the accuracy of the high-resolution image.

[1]  Michael Elad,et al.  Multiframe demosaicing and super-resolution of color images , 2006, IEEE Transactions on Image Processing.

[2]  Ioannis A. Kakadiaris,et al.  Improved face recognition using super-resolution , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[3]  Andrew Zisserman,et al.  Computer vision applied to super resolution , 2003, IEEE Signal Process. Mag..

[4]  Somchai Jitapunkul,et al.  A Robust Iterative Super-Resolution Reconstruction of Image Sequences using a Lorentzian Bayesian Approach with Fast Affine Block-Based Registration , 2007, 2007 IEEE International Conference on Image Processing.

[5]  Lisimachos P. Kondi,et al.  Resolution enhancement of video sequences with simultaneous estimation of the regularization parameter , 2004, J. Electronic Imaging.

[6]  Aggelos K. Katsaggelos,et al.  Iterative Image Restoration Algorithms , 1989 .

[7]  Panos E. Papamichalis,et al.  An adaptive M-estimation framework for robust image super resolution without regularization , 2008, Electronic Imaging.

[8]  Jan Flusser,et al.  Resolution enhancement via probabilistic deconvolution of multiple degraded images , 2006, Pattern Recognit. Lett..

[9]  Panos Papamichalis,et al.  Robust Color Image Superresolution: An Adaptive M-Estimation Framework , 2008, EURASIP J. Image Video Process..

[10]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[11]  Shmuel Peleg,et al.  Robust super-resolution , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Aggelos K. Katsaggelos,et al.  Bayesian resolution enhancement of compressed video , 2004, IEEE Transactions on Image Processing.

[13]  Alberto Del Bimbo,et al.  Superfaces: A Super-Resolution Model for 3D Faces , 2012, ECCV Workshops.

[14]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

[15]  Lisimachos P. Kondi,et al.  An image super-resolution algorithm for different error levels per frame , 2006, IEEE Transactions on Image Processing.

[16]  Nikolas P. Galatsanos,et al.  Super-Resolution Based on Fast Registration and Maximum a Posteriori Reconstruction , 2007, IEEE Transactions on Image Processing.

[17]  Masatoshi Okutomi,et al.  Robust and accurate estimation of multiple motions for whole-image super-resolution , 2008, 2008 15th IEEE International Conference on Image Processing.

[18]  Lisimachos P. Kondi,et al.  A fully robust framework for MAP image super-resolution , 2012, 2012 19th IEEE International Conference on Image Processing.

[19]  Lisimachos P. Kondi,et al.  On the improvement of image registration for high accuracy super-resolution , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Somchai Jitapunkul,et al.  A Lorentzian Stochastic Estimation for a Robust Iterative Multiframe Super-Resolution Reconstruction with Lorentzian-Tikhonov Regularization , 2007, EURASIP J. Adv. Signal Process..

[21]  Tuan Q. Pham,et al.  Robust super-resolution without regularization , 2008 .

[22]  Shin Ishii,et al.  Edge-Preserving Bayesian Image Superresolution Based on Compound Markov Random Fields , 2007, ICANN.

[23]  Christopher M. Bishop,et al.  Bayesian Image Super-Resolution , 2002, NIPS.

[24]  Ce Liu,et al.  A Bayesian Approach to Alignment-Based Image Hallucination , 2012, ECCV.

[25]  Stephen J. Roberts,et al.  Bayesian Image Super-resolution, Continued , 2006, NIPS.

[26]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.