Noise Removal via Bayesian Wavelet Coring
Published in:
Proc. 3rd International Conference on Image Processing
Lausanne, Switzerland. September, 1996.
doi; 10.1109/ICIP.1996.559512
© IEEE Signal Processing Society
More recent, full-length papers on wavelet denoising:
•
Bayesian denoising of visual images in the wavelet domain, Spring 1999.
•
Image denoising using a scale mixture of Gaussians in the wavelet domain, Fall 2003.
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.
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