Adaptive image denoising based on support vector machine and wavelet description

Adaptive image denoising based on support vector machine and wavelet description Adaptive image denoising method decomposes the original image into a series of basic pattern feature images on the basis of wavelet description and constructs the support vector machine regression function to realize the wavelet description of the original image. The support vector machine method allows the linear expansion of the signal to be expressed as a nonlinear function of the parameters associated with the SVM. Using the radial basis kernel function of SVM, the original image can be extended into a MEXICAN function and a residual trend. This MEXICAN represents a basic image feature pattern. If the residual does not fluctuate, it can also be represented as a characteristic pattern. If the residuals fluctuate significantly, it is treated as a new image and the same decomposition process is repeated until the residuals obtained by the decomposition do not significantly fluctuate. Experimental results show that the proposed method in this paper performs well; especially, it satisfactorily solves the problem of image noise removal. It may provide a new tool and method for image denoising. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optical Review Springer Journals

Adaptive image denoising based on support vector machine and wavelet description

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Publisher
Springer Journals
Copyright
Copyright © 2017 by The Optical Society of Japan
Subject
Physics; Optics, Lasers, Photonics, Optical Devices; Atomic, Molecular, Optical and Plasma Physics; Quantum Optics; Microwaves, RF and Optical Engineering
ISSN
1340-6000
eISSN
1349-9432
D.O.I.
10.1007/s10043-017-0360-9
Publisher site
See Article on Publisher Site

Abstract

Adaptive image denoising method decomposes the original image into a series of basic pattern feature images on the basis of wavelet description and constructs the support vector machine regression function to realize the wavelet description of the original image. The support vector machine method allows the linear expansion of the signal to be expressed as a nonlinear function of the parameters associated with the SVM. Using the radial basis kernel function of SVM, the original image can be extended into a MEXICAN function and a residual trend. This MEXICAN represents a basic image feature pattern. If the residual does not fluctuate, it can also be represented as a characteristic pattern. If the residuals fluctuate significantly, it is treated as a new image and the same decomposition process is repeated until the residuals obtained by the decomposition do not significantly fluctuate. Experimental results show that the proposed method in this paper performs well; especially, it satisfactorily solves the problem of image noise removal. It may provide a new tool and method for image denoising.

Journal

Optical ReviewSpringer Journals

Published: Sep 7, 2017

References

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