Deep neural networks have become invaluable tools for supervised machine learning, eg
classification of text or images. While often offering superior results over traditional …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

This book is about computational methods based on operator splitting. It consists of twenty-
three chapters written by recognized splitting method contributors and practitioners, and …

We propose simple and extremely efficient methods for solving the basis pursuit problem
\min{‖u‖_1:Au=f,u∈R^n\}, which is used in compressed sensing. Our methods are based …

Bibliography Page 1 Bibliography [1] V. Akcelik, G. Biros, A. Draganescu, J. Hill, O. Ghattas,
and B. Van Bloemen Waanders, Dynamic data-driven inversion for terascale simulations …

Imaging is an interdisciplinary research area with profound applications in many areas of
science, engineering, technology, and medicine. The most primitive form of imaging is visual …

In this paper we formulate a new time dependent convolutional model for super-resolution
based on a constrained variational model that uses the total variation of the signal as a …

A nonlocal quadratic functional of weighted differences is examined. The weights are based
on image features and represent the affinity between different pixels in the image. By …

We are motivated by a recently developed nonlinear inverse scale space method for image
denoising [M. Burger, G. Gilboa, S. Osher, and J. Xu, Commun. Math. Sci., 4 (2006), pp. 179 …

Three-dimensional imaging and image processing has become an important part for
investigations of fluid distribution and flow in porous media. We describe two methods of …