Restoring multisource degraded images based on wavelet-domain projection pursuit learning network

被引:0
|
作者
Lin, W [1 ]
Tian, Z [1 ]
Wen, XB [1 ]
机构
[1] Northwestern Polytech Univ, Xian 710072, Peoples R China
关键词
wavelet-domain projection pursuit learning network; wavelet shrinkage; multisource degraded image; restoring image;
D O I
10.1117/12.539067
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 no 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.
引用
收藏
页码:583 / 587
页数:5
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