Use of Motion Information in Super-Resolution Mosaicing

In this paper, we present a super-resolution (SR) method based on iterative backprojections. Motion information is used for the synthesis of the restoration filter in the SR method. Both, the blur estimation and the choice of the degradation model, are based on estimated global motion. The method is applied to highly under-sampled images such as DC images of MPEG compressed video and medical magnetic resonance (MR) images. Results of the comparison of our blur model with some common blur models are encouraging.

[1]  Shmuel Peleg,et al.  Restoration of multiple images with motion blur in different directions , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.

[2]  Takahiro Saito,et al.  Super-resolution sharpening-demosaicking method for removing image blurs caused by an optical low-pass filter , 2005, IEEE International Conference on Image Processing 2005.

[3]  A. Murat Tekalp,et al.  Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time , 1997, IEEE Trans. Image Process..

[4]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

[5]  Ofer Hadar,et al.  Super-resolution mosaicing from MPEG compressed video , 2005, ICIP.

[6]  Shree K. Nayar,et al.  Video super-resolution using controlled subpixel detector shifts , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Norman S. Kopeika,et al.  Image Resolution Limits Resulting From Mechanical Vibrations , 1985, Optics & Photonics.

[8]  Yitzhak Yitzhaky,et al.  Identification of blur parameters from motion-blurred images , 1996, Optics & Photonics.