||Image denoising in the wavelet domain is one of the most addressed problem in image processing in the last twentyyears, with thousands of papers, from the seminal work of Donoho to the more recent GSM based approaches.
We propose a novel approach for wavelet-based image denoising and we try to prove that an adaptive steered version of our wavelets can get better result than the non-adaptive ones.
In line with the majority of the works on wavelet denoising, we simplify our setting to the assumption of white Gaussian noise.
We start defining the Riesz transform, its properties and its use in conjunction with bandlimited wavelet frames. The resulting filters are steered according to the estimation of the local orientation we obtain from the Monogenic analysis of the image. We finally show the effect of steering in the simple case of soft-thresholding and in the more sophisticated SURE-LET