An improved method for joint image registration and superresolution

Superresolution reconstruction (SRR) is an effective approach for improving spatial resolution of image, which need not change the original imaging system hardware. Among the SRR methods, jointly estimate image registration and superresolution can overcome the serve aliased between low resolution images. However, further studies are still necessary for choose appropriate prior mode for better preserving edge of image. Also it is important for the choice of adaptive regularization parameters in the SRR based on regularization/maximum a posteriori (MAP) method. In this paper, we propose a method for joint image registration and SRR method based on MAP framework. In the reconstruction, the initial high resolution image is firstly obtained by the shift-and-add method. Then, the edge-preserved MRF model is used to improve the reconstructed image quality. Moreover, adaptive regularization method is used in the SRR. Experimental results demonstrate our improved SRR method effective.

[1]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[2]  Takeo Kanade,et al.  Limits on Super-Resolution and How to Break Them , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Michael Elad,et al.  Advances and challenges in super‐resolution , 2004, Int. J. Imaging Syst. Technol..

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

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

[6]  Michael Elad,et al.  A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur , 2001, IEEE Trans. Image Process..

[7]  Nikolas P. Galatsanos,et al.  Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images , 2006, IEEE Transactions on Image Processing.

[8]  Moon Gi Kang,et al.  Super-resolution image reconstruction , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[9]  Michael Elad,et al.  Generalizing the Nonlocal-Means to Super-Resolution Reconstruction , 2009, IEEE Transactions on Image Processing.

[10]  Shmuel Peleg,et al.  Image sequence enhancement using sub-pixel displacements , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Michel Barlaud,et al.  Deterministic edge-preserving regularization in computed imaging , 1997, IEEE Trans. Image Process..