Smooth Hard-Thresholding

Smooth Hard-Thresholding for Singular Values with Stein's Unbiased Risk Estimate

Announce Type: cross Abstract: Low-rank matrix denoising is a central primitive in patch-based image restoration and many other inverse problems. Classical SVD-based image denoising methods often choose a truncation rank by matching residual singular-value energy with an estimated noise energy, but this rule is not a finite-sample risk principle because a fitted low-rank approximation inevitably absorbs part of the noise. This paper develops a mathematically rigorous alternative based on...