Sure-Based Wavelet Thresholding Integrating Inter-Scale Dependencies

We propose here a new pointwise wavelet thresholding function that incorporates inter-scale dependencies. This non-linear function depends on a set of four linear parameters per sub-band which are set by minimizing Stein´s unbiased MSE estimate (SURE). Our approach assumes additive Gaussian white noise. In order for the inter-scale dependencies to be faithfully taken into account, we also develop a rigorous feature alignment processing, that is adapted to arbitrary wavelet filters (e.g. non-symmetric filters). Finally, we demonstrate the efficiency of our denoising approach in simulations over a wide range of noise levels for a representative set of standard images.