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Proceedings Paper

Gaussian kernel-based variable-grid image super-resolution reconstruction
Author(s): Cheng Zhou; Yi-hua Tan; Jin-wen Tian; Wen-po Ma
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Paper Abstract

A new method for super-resolution reconstruction based on the Gaussian-kernel is presented. Each pixel is modeled as a Gaussian distribution to reconstruct, which is iterated by the image weighting parameter adaptively. The parallelism of this real-valued algorithm based on the grid model enables better integration of the information of the low-resolution images of the same scene. Compared to the bi-cubic interpolation algorithm, experiments show that the proposed algorithm can achieve a gain up over 1.0dB. The visual quality of presented algorithm demonstrate the recovery of spatial frequencies above the band-limit and corresponding reduction in ringing artifacts when compared with the bicubic interpolation algorithm. And the proposed method gets better objective and subjective quality by preserving the sharpness of the edges.

Paper Details

Date Published: 30 October 2009
PDF: 5 pages
Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749808 (30 October 2009); doi: 10.1117/12.831331
Show Author Affiliations
Cheng Zhou, Huazhong Univ. of Science and Technology (China)
Yi-hua Tan, Huazhong Univ. of Science and Technology (China)
Jin-wen Tian, Huazhong Univ. of Science and Technology (China)
Wen-po Ma, Beijing Institute of Space Mechanics and Electricity (China)


Published in SPIE Proceedings Vol. 7498:
MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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