A simple and efficient wavelet-based denoising algorithm using joint interand intrascale statistics adaptively
J. Ge, Gagan Mirchandani · 2003
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.