Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

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Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to mi ...

We develop theory and methodology for the problem of nonparametric registration of functional data that have been subjected to random deformation (warping) of their time scale. The separation of this phase variation ("horizontal" variation) from the amplit ...

Ill-posed linear inverse problems are frequently encountered in image reconstruction tasks. Image reconstruction methods that combine the Plug-and-Play (PnP) priors framework with convolutional neural network (CNN) based denoisers have shown impressive per ...

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Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been de ...

Purpose Non-invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill-posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. Instead, ActiveAx uses a 3D sampling scheme to e ...

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We present a systematic method to expand in components four dimensional superconformal multiplets. The results cover all possible N = 1 multiplets and some cases of interest for N = 2. As an application of the formalism we prove that certain N = 2 spinning ...

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

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Large Steps in Inverse Rendering of Geometry

Functional registration and local variations: Identifiability, rank, and tuning

Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Deconvolution of Sustained Neural Activity From Large-Scale Calcium Imaging Data

Einstein-Cartan gravity, matter, and scale-invariant generalization

Fast Least-Squares Padé approximation of problems with normal operators and meromorphic structure

Deep-learning projector for optical diffraction tomography

ActiveAx(ADD): Toward non-parametric and orientationally invariant axon diameter distribution mapping using PGSE

Good Vibrations and Unknown Excitations

On the density of the supremum of the solution to the linear stochastic heat equation

Differential operators for superconformal correlation functions