A new bivariate MAP estimator for DT-CWT-based video denoising
A new bivariate maximum a posteriori estimator is proposed for the magnitude components of the dual-tree complex wavelet transform (DT-CWT) coefficients in order to reduce additive white Gaussian noise in a video. The estimator considers the fact that the magnitude components of the DT-CWT coefficients of the Gaussian distributed noise fit the generalized Gamma distribution very well [1]. For spatial filtering, the joint distribution function of the magnitude components of the DT-CWT coefficients of the two neighboring frames of a video is considered to be locally-adaptive bivariate Gaussian having a non-negative mean. The correlation coefficient of this distribution function acts as an indirect measure of the motion of the DT-CWT coefficients between two neighboring frames. A recursive time averaging of the spatially filtered magnitude components is adopted for further noise reduction. Experimental results on test video sequences show that the proposed estimator provides an average peak signal-to-noise ratio that is higher than that provided by the others.