Noise Removal via Bayesian Wavelet Coring
Eero P. Simoncelli and Edward H. Adelson
Proceedings 3rd International Conference on Image Processing
Lausanne, Switzerland; September, 1996.
The classical solution to the noise removal problem is the Wiener
filter, which utilizes the second-order statistics of the Fourier
decomposition. Subband decompositions of natural images have
significantly non-Gaussian higher-order point statistics; these statistics
capture image properties that elude Fourier-based techniques. We
develop a Bayesian estimator that is a natural extension of the Wiener
solution, and that exploits these higher-order statistics. The
resulting nonlinear estimator performs a "coring" operation. We provide a simple model for the subband statistics,
and use it to develop a semi-blind noise-removal algorithm based on a steerable wavelet pyramid.