A hybrid wavelet-spatial denoising filter

Noise is one of the most widespread problems present in nearly all imaging applications. The search for efficient image denoising methods is still a valid challenge. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. They all show an outstanding performance when the image model corresponds to the algorithm assumptions, but fail in general and create artifacts or remove fine image structures. Therefore, a universal “best” filter has yet to be found. Wavelet analysis is a new method consisting of a set basis functions that can be used to analyze signals in both time (or space) and frequency domains simultaneously. In this paper, a novel hybrid filter for image despeckling that combines wavelet denoising and an enhanced adaptive Kuan filter is proposed, resulting in a significant gain with respect to many spatial as well as wavelet-based speckle reduction filters.