IEEE Trans Image Process - Efficient image denoising method based on a new adaptive wavelet packet thresholding function.

Tópicos

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Resumo

This paper proposes a statistically optimum adaptive wavelet packet (WP) thresholding function for image denoising based on the generalized Gaussian distribution. It applies computationally efficient multilevel WP decomposition to noisy images to obtain the best tree or optimal wavelet basis, utilizing Shannon entropy. It selects an adaptive threshold value which is level and subband dependent based on analyzing the statistical parameters of subband coefficients. In the utilized thresholding function, which is based on a maximum a posteriori estimate, the modified version of dominant coefficients was estimated by optimal linear interpolation between each coefficient and the mean value of the corresponding subband. Experimental results, on several test images under different noise intensity conditions, show that the proposed algorithm, called OLI-Shrink, yields better peak signal noise ratio and superior visual image quality-measured by universal image quality index-compared to standard denoising methods, especially in the presence of high noise intensity. It also outperforms some of the best state-of-the-art wavelet-based denoising techniques.

Resumo Limpo

paper propos statist optimum adapt wavelet packet wp threshold function imag denois base general gaussian distribut appli comput effici multilevel wp decomposit noisi imag obtain best tree optim wavelet basi util shannon entropi select adapt threshold valu level subband depend base analyz statist paramet subband coeffici util threshold function base maximum posteriori estim modifi version domin coeffici estim optim linear interpol coeffici mean valu correspond subband experiment result sever test imag differ nois intens condit show propos algorithm call olishrink yield better peak signal nois ratio superior visual imag qualitymeasur univers imag qualiti indexcompar standard denois method especi presenc high nois intens also outperform best stateoftheart waveletbas denois techniqu

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