Context-based bias removal of statistical models of wavelet coefficients for image denoising

Existing wavelet-based image denoising techniques all assume a probability model of wavelet coefficients that has zero mean, such as zero-mean Laplacian, Gaussian, or generalized Gaussian distributions. While such a zero-mean probability model fits a wavelet subband well, in areas of edges and textures the distribution of wavelet coefficients exhibits a significant bias. We propose a context modeling technique to estimate the expectation of each wavelet coefficient conditioned on the local signal structure. The estimated expectation is then used to shift the probability model of wavelet coefficient back to zero. This bias removal technique can significantly improve the performance of existing wavelet-based image denoisers.