Quadratic Volterra filters and wavelet thresholding: two new approaches for noise reduction in images degraded by multiplicative signal-dependent noise

Details Quadratic Volterra filters and wavelet thresholding: two new approaches for noise reduction in images degraded by multiplicative signal-dependent noise Quadratic Volterra filters and wavelet thresholding: two new approaches for noise reduction in images degraded by multiplicative signal-dependent noise

Oct 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3164...49d&link_type=abstract

Proc. SPIE Vol. 3164, p. 49-58, Applications of Digital Image Processing XX, Andrew G. Tescher; Ed.

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In inertial confinement fusion experiments, images of neutron distributions provided by a large aperture imaging system are degraded by multiplicative and signal-dependent noise. In this case, it is difficult to separate the object's fluctuations from those of the noise. Wiener filtering is a well-known method which takes noise into account. Unfortunately, the Wiener filter is a low-pass filter which blurs the contours. The propose of this paper is to introduce two new approaches which restore most of the object's fluctuations. The first approach is a Wiener adaptive filtering method that consists of locally adjusting the value of the cutoff frequency. This adjustment is based on the basic properties of the human visual system where the visibility of noise can be reduced considerably in the neighborhood of a 'strong' contour. The contour detection is realized by means of a special class of quadratic Volterra filters. These filters are approximately equivalent to the product of a local mean estimator with a high pass filter. The second approach consists in applying a multiresolution decomposition of the raw image followed by a wavelet thresholding that changes for each resolution layer. Results and comparative evaluation of these two methods will also be presented.

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