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Deep Convolutional Neural Network for Inverse Problems in Imaging
Kyong Hwan Jin, Michael T. McCann, Emmanuel Froustey +1
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as t…
Scaling Limits of Solutions of SPDE Driven by Lévy White Noises
Julien Fageot, Michael Unser
Consider a random process s solution of the stochastic partial differential equation Ls = w with L a homogeneous operator and w a multidimensional Lévy white noise. In this paper,…
Back-propagating the light of field stars to probe telescope mirrors aberrations
Ferréol Soulez, Frédéric Courbin, Michael Unser
We propose a wavefront-based method to estimate the PSF over the whole field of view. This method estimate the aberrations of all the mirrors of the telescope using only field star…
Multidimensional Lévy White Noises in Weighted Besov Spaces
Julien Fageot, Alireza Fallah, Michael Unser
In this paper, we study the Besov regularity of d-dimensional Lévy white noises. More precisely, we describe new sample paths properties of a given white noise in terms of weighted…
Splines are Universal Solutions of Linear Inverse Problems with Generalized-TV regularization
Michael Unser, Julien Fageot, John Paul Ward
Splines come in a variety of flavors that can be characterized in terms of some differential operator L. The simplest piecewise-constant model corresponds to the derivative operato…