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30 October 2009 A novel superresolution algorithm based on standard displacements
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Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74981Z (2009)
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
In many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. In order to enhance the existed image resolution, several approaches to solve this problem have been investigated previously, such as maximum a posteriori probability (MAP), projection onto convex sets(POCS) etc. Those algorithms enhance reconstruct high resolution with reduced aliasing, from a sequence of undersampled frames. But whether POCS, or MAP estimator in space domain, image pixels are rearranged by using lexicographic ordering as a large matrix in procession. These methods have to solve a large ill-condition equation group, which leads to a big burden of computation and storage, complexity of algorithm. So they are rarely used in practical application. In order to solve these problems, a novel reconstruction high resolution(HR) image algorithm based on the standard displacements of low resolution(LR) images is proposed. Moreover, a set of recursive updating algorithm models is presented. The results of simulating experiments show that the resolution, the details as well as the definition of the high resolution image given by using our method are greatly enhanced. At the same time, the running speed of our method is greatly faster than other super-resolution methods.
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Xiangquan Wei, Maozu Guo, Jianming Huang, and Feng Chen "A novel superresolution algorithm based on standard displacements", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74981Z (30 October 2009);

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