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

Wei Lin; Zheng Tian; Xianbin Wen

doi:10.1117/12.539067

25 September 2003 Restoring multisource degraded images based on wavelet-domain projection pursuit learning network

Wei Lin, Zheng Tian, Xianbin Wen

Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.539067
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China

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.

Citation Download Citation

Wei Lin, Zheng Tian, and Xianbin Wen "Restoring multisource degraded images based on wavelet-domain projection pursuit learning network", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); https://doi.org/10.1117/12.539067

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KEYWORDS

Wavelets

Visualization

Image processing

Digital filtering

Point spread functions

Signal to noise ratio

Image filtering

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