Improved IBP for Super-resolving Remote Sensing Images

Abstract The research on super-resolution (SR) image recovery has been carried out in the last two decades. With the fast development of computer technology, more and more efficient algorithms have been put forward in recent years. The Iteration Back Projection (IBP) method is one of the popular methods with SR. In this paper, a modified IBP is proposed for remote sensing image processing. This improved IBP can efficiently deal with local affine transformations within images for SR. Experiments and results are presented using both a synthetic set of images generated from a single Landsat ETM+ channel and a set of Advanced Land Observing Satellite (ALOS) imagery.

[1]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[2]  Senthil Periaswamy,et al.  Image Registration for MRI , 2002 .

[3]  Hua Han,et al.  Wavelet-domain HMT-based image super-resolution , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[4]  Hany Farid,et al.  Elastic registration in the presence of intensity variations , 2003, IEEE Transactions on Medical Imaging.

[5]  Robert L. Stevenson,et al.  Super-resolution from image sequences-a review , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).

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

[7]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[8]  A. Ardeshir Goshtasby,et al.  2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications , 2005 .

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

[10]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.