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08/21/2023
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
We propose to learn non-convex regularizers with a prescribed upper boun...
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03/31/2023
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
In supervised learning, the regularization path is sometimes used as a c...
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11/22/2022
A Neural-Network-Based Convex Regularizer for Image Reconstruction
The emergence of deep-learning-based methods for solving inverse problem...
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11/11/2022
Self-Supervised Isotropic Superresolution Fetal Brain MRI
Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMR...
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10/28/2022
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Lipschitz-constrained neural networks have several advantages compared t...
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08/16/2022
Delaunay-Triangulation-Based Learning with Hessian Total-Variation Regularization
Regression is one of the core problems tackled in supervised learning. R...
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06/29/2022
From Kernel Methods to Neural Networks: A Unifying Variational Formulation
The minimization of a data-fidelity term and an additive regularization ...
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06/17/2022
The Role of Depth, Width, and Activation Complexity in the Number of Linear Regions of Neural Networks
Many feedforward neural networks generate continuous and piecewise-linea...
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06/14/2022
Stability of image reconstruction algorithms
Robustness and stability of image reconstruction algorithms have recentl...
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06/07/2022
Asymptotic Stability in Reservoir Computing
Reservoir Computing is a class of Recurrent Neural Networks with interna...
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04/28/2022
Bona fide Riesz projections for density estimation
The projection of sample measurements onto a reconstruction space repres...
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04/13/2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Lipschitz-constrained neural networks have many applications in machine ...
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03/18/2022
Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors
Most modern imaging systems involve a computational reconstruction pipel...
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03/10/2022
Stable Parametrization of Continuous and Piecewise-Linear Functions
Rectified-linear-unit (ReLU) neural networks, which play a prominent rol...
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03/04/2022
Ridges, Neural Networks, and the Radon Transform
A ridge is a function that is characterized by a one-dimensional profile...
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12/27/2021
Sparsest Univariate Learning Models Under Lipschitz Constraint
Beside the minimization of the prediction error, two of the most desirab...
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12/12/2021
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total-Variation
In this paper, we introduce the Hessian-Schatten total-variation (HTV) –...
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12/16/2020
Shortest Multi-Spline Bases for Generalized Sampling
Generalized sampling consists in the recovery of a function f, from the ...
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10/26/2020
Optimal transport-based metric for SMLM
We propose the use of Flat Metric to assess the performance of reconstru...
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09/24/2020
Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging
Quantitative phase imaging (QPI) is an emerging label-free technique tha...
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08/07/2020
Generating Sparse Stochastic Processes Using Matched Splines
We provide an algorithm to generate trajectories of sparse stochastic pr...
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01/17/2020
Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant
We introduce a variational framework to learn the activation functions o...
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10/03/2019
Time-Dependent Deep Image Prior for Dynamic MRI
We propose a novel unsupervised deep-learning-based algorithm to solve t...
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04/24/2019
Native Banach spaces for splines and variational inverse problems
We propose a systematic construction of native Banach spaces for general...
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03/02/2019
A unifying representer theorem for inverse problems and machine learning
The standard approach for dealing with the ill-posedness of the training...
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01/14/2019
Self-Supervised Deep Active Accelerated MRI
We propose to simultaneously learn to sample and reconstruct magnetic re...
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12/19/2018
Pocket Guide to Solve Inverse Problems with GlobalBioIm
GlobalBioIm is an open-source MATLAB library for solving inverse problem...
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11/02/2018
An L1 Representer Theorem for Multiple-Kernel Regression
The theory of RKHS provides an elegant framework for supervised learning...
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10/23/2018
Principled Design and Implementation of Steerable Detectors
We provide a complete pipeline for the detection of patterns of interest...
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09/19/2018
Direct Reconstruction of Saturated Samples in Band-Limited OFDM Signals
Given a set of samples, a few of them being possibly saturated, we propo...
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06/12/2018
Fast Rotational Sparse Coding
We propose an algorithm for rotational sparse coding along with an effic...
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03/15/2018
Diverse M-Best Solutions by Dynamic Programming
Many computer vision pipelines involve dynamic programming primitives su...
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02/26/2018
A representer theorem for deep neural networks
We propose to optimize the activation functions of a deep neural network...
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02/06/2018
Fast Piecewise-Affine Motion Estimation Without Segmentation
Current algorithmic approaches for piecewise affine motion estimation ar...
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02/05/2018
Continuous-Domain Solutions of Linear Inverse Problems with Tikhonov vs. Generalized TV Regularization
We consider linear inverse problems that are formulated in the continuou...
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09/06/2017
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
We present a new method for image reconstruction which replaces the proj...
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07/11/2017
Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography
Optical diffraction tomography relies on solving an inverse scattering p...
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05/16/2017
Learning Convex Regularizers for Optimal Bayesian Denoising
We propose a data-driven algorithm for the maximum a posteriori (MAP) es...
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11/11/2016
Deep Convolutional Neural Network for Inverse Problems in Imaging
In this paper, we propose a novel deep convolutional neural network (CNN...
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12/07/2015
On The Continuous Steering of the Scale of Tight Wavelet Frames
In analogy with steerable wavelets, we present a general construction of...
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09/14/2012
Hessian Schatten-Norm Regularization for Linear Inverse Problems
We introduce a novel family of invariant, convex, and non-quadratic func...
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07/22/2011
On the Hilbert transform of wavelets
A wavelet is a localized function having a prescribed number of vanishin...
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05/21/2011
Fast O(1) bilateral filtering using trigonometric range kernels
It is well-known that spatial averaging can be realized (in space or fre...
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03/10/2010
Fast space-variant elliptical filtering using box splines
The efficient realization of linear space-variant (non-convolution) filt...
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08/26/2009
Fast adaptive elliptical filtering using box splines
We demonstrate that it is possible to filter an image with an elliptic w...
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08/26/2009
Gabor wavelet analysis and the fractional Hilbert transform
We propose an amplitude-phase representation of the dual-tree complex wa...
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08/24/2009
Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-like Transforms
We propose a novel method for constructing Hilbert transform (HT) pairs ...
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08/24/2009