Performance of First-Order Algorithms for TV Penalized Weighted Least-Squares Denoising Problem

Denoising of images perturbed by non-standard noise models (e.g., Poisson or Gamma noise) can be often realized by a sequence of penalized weighted least-squares minimization problems. In the recent past, a variety of first-order algorithms have been proposed for convex problems but their efficiency is usually tested with the classical least-squares data fidelity term. Thus, in this manuscript, first-order state-of-the-art computational schemes are applied on a total variation penalized weighted least-squares denoising problem and their performance is evaluated on numerical examples simulating a Poisson noise perturbation.

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