De-Noising via Wavelet Transforms Using Steerable Filters

| Feature extraction remains an important part of low-level vision. Traditional oriented lters have been e ective tools to identify features, such as lines and edges. Steerable lters, which can be adjusted at arbitrary orientation, have made decisions of feature orientations more precise. Combined with a pyramid structure of a multiscale representation, these lters can provide a reliable and e cient tool for image analysis. This paper takes advantage of multiscale steerable lters in the context of de-noising. First a set of novel lters are designed, that decompose the frequency plane into distinct directional bands. Next, we identify the dominant direction and strength at each point of an image from quadrature pairs of steerable lters. A nonlinear threshold function is then applied to the ltered coe cients to suppress noise. The denoised image is restored from coe cients modi ed at each level of transform space. We demonstrate the bene ts of multiscale steerable lters for de-noising and show that it can greatly reduce noise while preserving image features. Two examples are presented to verify the e cacy of