An improved algorithm of image registration based on least squares adjustment

References

• Chen, Y. -J., Sun, G. -I. and Yao, H. 2012. Hybrid spatial-frequency domain sub – pixel image registration with wavelet pyramid. Journal of Shanghai University (Natural Science), 18(4), pp.342–8.
   Google Scholar
• Crum, W. R., Hartkens, T. and Hill, D. 2014. Nonrigid image registration:theory and practice. British Journal of Radiology, 77(s2), pp. S140–53.
   Google Scholar
• Deshmukh, M. and Bhosle, U. 2011. A survey of image registration. International Journal of Image Processing, 5(3), p.245.
   Google Scholar
• Elad, M. and Feuer, A. 1999. Superresolution restoration of an image sequence: adaptive filtering approach. IEEE Transactions on Image Processing, 8(3), pp.387–95. doi: 10.1109/83.748893
   PubMed Web of Science ®Google Scholar
• Goncalves, H., Corte-Real, L. and Goncalves, J. A. 2011. Automatic image registration through image segmentation and sift. IEEE Transactions on Geoscience and Remote Sensing, 49(7), pp.2589–600. doi: 10.1109/TGRS.2011.2109389
   Web of Science ®Google Scholar
• Goshtasby, A. A. 2012. Image registration methods', image registration. Berlin: Springer.
   Google Scholar
• Goshtasby, A. and Le Moign, J. 2012. Image registration. Berlin: Springer.
   Google Scholar
• Han, L., Huang, C., Xu, M. and Zheng, S. 2013. Parameter-adaptive approach to image sub-pixel registration. Journal of Computer Applications, 33(2), pp.487–90. doi: 10.3724/SP.J.1087.2013.00487
   Google Scholar
• Huang, S. -Y., Yong, Y. and Wang, G. -Y. 2013. Anisotropic fourth-order diffusion regularization for multiframe super-resolution reconstruction. Journal of Central South University, 20(11), pp.3180–6. doi: 10.1007/s11771-013-1842-y
   Web of Science ®Google Scholar
• Izadpanahi, S., Demirel, H. 2012. Multi-frame super resolution using edge directed interpolation and complex wavelet transform. IET Conference on Image Processing, pp. 1–5. London, UK.
   Google Scholar
• Keren, D., Peleg, S. and Brada, R. 1988. Image sequence enhancement using sub-pixel displacements. Computer Vision and Pattern Recognition, 1988. Proceedings CVPR'88., Computer Society Conference on IEEE. Ann Arbor, MI, USA.
   Google Scholar
• Le Moigne, J., Netanyahu, N. S. and Eastman, R. D. 2011. Image registration for remote sensing. Cambridge: Cambridge University Press.
   Google Scholar
• Li, X. and Ma, X. 2009. Image registration based on phase-correlation and Keren algorithm. Microcomputer Applications, 30(011), pp.19–23.
   Google Scholar
• Liu, Z., An, J. and Jing, Y. 2012. A simple and robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration. Transactions on Geoscience and Remote Sensing, 50(2), pp.514–27. doi: 10.1109/TGRS.2011.2160645
   Web of Science ®Google Scholar
• Lucas, B. D. and Kanade, T. 1981. An iterative image registration technique with an application to stereo vision. Proceedings of the Imaging Understanding Workshop. San Francisco, CA, USA.
   Google Scholar
• Milchevski, A., Ivanovski, Z. and Mustafa, B. 2011. Machine learning based supper-resolution algorithm robust to registration errors. Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE. Sedona, AZ, USA.
   Google Scholar
• Ng, M. K., Shen, H., Lam, E. Y. and Zhang, L. 2007. A total variation regularization based super-resolution reconstruction algorithm for digital video. EURASIP Journal on Advances in Signal Processing, 2007, p.74585.
   Web of Science ®Google Scholar
• Pang, Y., Gu, L., Ren, R. and Sun, J. 2013. An effective method of image registration for super-resolution. SPIE Optical Engineering+ Applications. (International Society for Optics and Photonics, 2013), San Diego, CA, USA.
   Google Scholar
• Park, S. C., Park, M. K. and Kang, M. G. 2003. Super-resolution image reconstruction: a technical overview. Signal Processing Magazine IEEE, 20(3), pp.21–36. doi: 10.1109/MSP.2003.1203207
   Web of Science ®Google Scholar
• Protter, M., Elad, M., Takeda, H. and Milanfar, P. 2009. Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Transactions on Image Processing, 18(1), pp.36–51. doi: 10.1109/TIP.2008.2008067
   PubMed Web of Science ®Google Scholar
• Rasti, P., Demirel, H. and Anbarjafari, G. 2013. Iterative back projection based image resolution enhancement. Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on IEEE. Zanjan, Iran.
   Google Scholar
• Sotiras, A., Davatzikos, C. and Paragios, N. 2013. Deformable medical image registration: a survey. IEEE Transactions on Medical Imaging, 32(7), pp.1153–90. doi: 10.1109/TMI.2013.2265603
   PubMed Web of Science ®Google Scholar
• Spinoulas, L., Katsaggelos, A. K., Jang, J., Yoo, Y., Im, J. and Paik, J. 2014. Defocus-invariant image registration for phase-difference detection auto focusing. Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on IEEE. JeJu Island, South Korea.
   Google Scholar
• Stark, H. and Oskoui, P. 1989. High-resolution image recovery from image-plane arrays, using convex projections. Journal of the Optical Society of America A, 6(11), pp.1715–26. doi: 10.1364/JOSAA.6.001715
   PubMedGoogle Scholar
• Vandewalle, P., Sü, S. and Vetterli, M. 2006. A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP Journal on Advances in Signal Processing, 2006, p.071459. doi: 10.1155/ASP/2006/71459
   Google Scholar
• Zhu, J., Zhou, C., Fan, D. and Zhou, J. 2014. A new method for superresolution image reconstruction based on surveying adjustment. Journal of Nanomaterials, 2014, p.2.
   Web of Science ®Google Scholar
• Zhu, Z. -W. and Zhou, J. -J. 2011. Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique. Journal of Central South University of Technology, 18, pp.809–15. doi: 10.1007/s11771-011-0766-7
   Web of Science ®Google Scholar