CALATRONI L.

We consider a stochastic gradient descent (SGD) algorithm for solving linear inverse problems (e.g., CT image reconstruction) in the Banach space framework of variable exponent Lebesgue spaces ppnq pRq. Such non-standard spaces have been recently proved to be the appropriate functional framework to enforce pixel-adaptive regularisation in...

fA fb fs ft fr fa fc ft. We propose and analyze an accelerated iterative dual diagonal descent algorithm for the solution of linear inverse problems with strongly convex regularization and general data-fit functions. We develop an inertial approach of which we analyze both convergence and stability properties. Using tools from inexact proximal...

We consider a variational model for Single Molecule Localisation Microscopy (SMLM) super-resolution. More specifically, we study a generalization of the Continuous Exact ell-{0} (CELO) penalty, recently introduced to relax the ell-{2}-ell-{0} problem, where a weighted-ell-{2} data fidelity now models signal-dependent Poisson noise. For the...

We propose a scaled adaptive version of the Fast Iterative Soft-Thresholding Algorithm, named S-FISTA, for the efficient solution of convex optimization problems with sparsity-enforcing regularization. S-FISTA couples a non-monotone backtracking procedure with a scaling strategy for the proximal–gradient step, which is particularly effective in...