A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
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ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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IEEE Transactions on Signal Processing - Part I
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IEEE Transactions on Image Processing
Statistical texture retrieval in noise using complex wavelets
Image Communication
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This paper proposes a method to estimate the parameters of the relative phase probability density function (RP pdf) of the complex coefficients when an image is corrupted by additive white Gaussian noise. With the complex Gaussian scale mixture (CGSM) assumption of the clean coefficients, we first introduce the relative phase mixture (RPM) pdf by deriving the pdf of the relative phase of the noisy coefficients. Along with the derived pdf, a parameter estimation method based on the maximum likelihood approach is proposed by exploiting the relationships between the pdf's parameters and the complex covariance matrix of the corresponding complex coefficient vector. Simulation studies using simulated data are performed to show the effectiveness of the estimation method. Moreover, we use the proposed estimation method in the application of texture retrieval in a noisy environment. The results show that the proposed method can estimate the parameters of the clean vector well in the case of simulated data and that the proposed estimation method improves the retrieval accuracy rate.