Adaptive parameter selection for block wavelet-thresholding deconvolution

In this paper, we propose a data-driven block thresholding procedure for wavelet-based non-blind deconvolution. The approach consists in appropriately writing the problem in the wavelet domain and then selecting both the block size and threshold parameter at each resolution level by minimizing Stein's unbiased risk estimate. The resulting algorithm is simple to implement and fast. Numerical illustrations are provided to assess the performance of the estimator.