Image Denoising via Adjustment of Wavelet Coefficient Magnitude
Correlation
To appear in:
Proc 7th IEEE Int'l Conf on Image Processing
Vancouver, 10-13 September 2000.
doi: 10.1109/ICIP.2000.899349
© IEEE Computer Society.
We describe a novel method of removing additive white noise of known
variance from photographic images. The method is based on a
characterization of statistical properties of natural images
represented in a complex wavelet decomposition. Specifically, we
decompose the noisy image into wavelet subbands, estimate the
autocorrelation of both the noise-free raw coefficients and their
magnitudes within each subband, impose these statistics by projecting
onto the space of images having the desired autocorrelations, and
reconstruct an image from the modified wavelet coefficients. This
process is applied repeatedly, and can be accelerated to produce
optimal results in only a few iterations. Denoising results compare
favorably to three reference methods, both perceptually and in terms
of mean squared error.
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