Poisson-Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian ...Poisson-Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson-Gaussian noise reduction algorithm has shown remark- able success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson-Gaussian noise into an additive white Gaussian noise （AWGN）. So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error （MMSE） estimator for logistic distribution in Poisson-Gaussian noise. The experimental results show that the proposed method yields good denoising results.展开更多
文摘Poisson-Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson-Gaussian noise reduction algorithm has shown remark- able success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson-Gaussian noise into an additive white Gaussian noise （AWGN）. So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error （MMSE） estimator for logistic distribution in Poisson-Gaussian noise. The experimental results show that the proposed method yields good denoising results.
