We consider the problem of robust deconvolution, and particularly recovery an unknown deterministic signal convolved with a known filter corrupted by additive noise. present novel, non-iterative data-driven approach. Specifically, our algorithm works in frequency-domain, where it tries to mimic optimal unrealizable non-linear Wiener-like as if were known. This leads threshold-type regularized e...
