A simple and efficient wavelet-based denoising algorithm using joint interand intrascale statistics adaptively

We propose a simple and efficient image denoising algorithm in the wavelet domain. The algorithm adaptively weighs the joint inter- and intrascale statistics of detail coefficients. Direct correlation of detail coefficients across scales is used to select the significant coefficients. Intrascale statistics are used to adaptively modify the coefficients, using a new homogeneity measure. Unlike existing algorithm using parametric models, prior knowledge and estimation of parameters are not needed. New justification is provided for the choice of the ´most regular´ wavelet derived from B-splines. The implementation is simple and efficient, with a performance comparable to results by state-of-art methods.