Robust neural network threshold determination for wavelet shrinkage in images

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

Proceedings of the 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems, CIS 2011

First Page

63

Last Page

68

Publication Date

12-9-2011

Abstract

The discrete wavelet transform (DWT) has been established as an effective tool in denoising images. Various studies have developed statistical models for denoising signals in the wavelet domain. In these techniques, the amount of noise is estimated from the detail coefficients of the transform. However, in images rich in textures, this estimate does not accurately reflect the noise levels of the image. In this paper, we introduce a robust method of noise and signal estimation using directional characteristics of an image. A feed-forward neural network is utilized to establish the relationship between the new estimators and the optimal soft threshold. Testing results show equivalent performance to traditional thresholding algorithms in most images. In highly detailed images, the proposed network shows significant improvement in denoising. © 2011 IEEE.

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Digitial Object Identifier (DOI)

10.1109/ICCIS.2011.6070303

Disciplines

Electrical and Electronics | Systems and Communications

Keywords

Noise control; Neural networks (Computer science)

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