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Proceedings Paper

Restoring multisource degraded images based on wavelet-domain projection pursuit learning network
Author(s): Wei Lin; Zheng Tian; Xianbin Wen
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Paper Abstract

The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposed for resolving the difficult task that restoring image, which is blurred by multisource degraded factors image. The new approach combines the advantages of both the projection pursuit and the wavelet shrinkage technique. By separately processing wavelet coefficients and scale coefficients, the WDPPLN resolves the problem of restoring image very well, when little or not a prior information about the degradation is available. The WDPPLN estimates the degraded factor, which blurred the image, using Projection Pursuit Learning Network (PPLN). Also, it suppresses the noise using the soft-threshold of the wavelet shrinkage technique. The new method is compared with the traditional methods and the PPLN method in visual effect and objective evaluation criterion. Experimental results show that it is an effective method for restoring multisource degraded image.

Paper Details

Date Published: 25 September 2003
PDF: 5 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.539067
Show Author Affiliations
Wei Lin, Northwestern Polytechnic Univ., (China)
Institute of Automation, CAS (China)
Huazhong Univ. of Science and Technology (China)
Zheng Tian, Northwestern Polytechnic Univ. (China)
Institute of Automation, CAS (China)
Huazhong Univ. of Science and Technology (China)
Xianbin Wen, Northwestern Polytechnic Univ. (China)
Institute of Automation, CAS (China)
Huazhong Univ. of Science and Technology (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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