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Proceedings Paper

Color image superresolution using multichannel data fusion
Author(s): Shubin Zhao; Hua Han; Silong Peng
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Paper Abstract

This paper presents a wavelet-domain Hidden Markov Tree(HMT)-based color image superresolution algorithm using multi-channel data fusion. Because there exists correlations among the three channels of a RGB color image, a channel by channel superresolution method almost certain leads to color distortion. In order to solve this problem, first the low-resolution color image is converted into a gray-scale image using the spatially-adaptive approach presented in this paper and the resulting gray-scale image must reflect the human perception of edges in the color image; then by superresolving this gray-scale image, a high-resolution image is obtained; finally, wavelet-domain HMT-based image superresolutions are performed for the three channels of the low-resolution color image using the same posterior state probabilities, which reflect the hidden states of the wavelet coefficients of the high-resolution gray-scale image obtained before, and thus the resulting high-resolution color image is what we desired. Becasue the correlations among the three channels of a RGB color image are considered, there are no color distortions in the reconstructed high-resolution image. Experimental results show that the reconstructed color images have high PSNR and are of high visual quality.

Paper Details

Date Published: 25 September 2003
PDF: 6 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538683
Show Author Affiliations
Shubin Zhao, Institute of Automation, CAS (China)
Hua Han, Institute of Automation, CAS (China)
Silong Peng, Institute of Automation, CAS (China)


Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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