2.6k citations · 4.8k across the 91 of their papers we have counts for
102 papers
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
Alexis Goujon, Sebastian Neumayer, Michael Unser
We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus. Such regularizers give rise to variational denoisers that minimize a conv…
Optical Diffraction Tomography Meets Fluorescence Localization Microscopy
Thanh-An Pham, Emmanuel Soubies, Ferréol Soulez +1
We show that structural information can be extracted from single molecule localization microscopy (SMLM) data. More precisely, we reinterpret SMLM data as the measures of a phasele…
A Mini-Batch Quasi-Newton Proximal Method for Constrained Total-Variation Nonlinear Image Reconstruction
Tao Hong, Thanh-an Pham, Irad Yavneh +1
Over the years, computational imaging with accurate nonlinear physical models has garnered considerable interest due to its ability to achieve high-quality reconstructions. However…
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
Sebastian Neumayer, Lénaïc Chizat, Michael Unser
In supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this pap…
Self-Supervised Isotropic Superresolution Fetal Brain MRI
Kay Lächler, Hélène Lajous, Michael Unser +2
Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thi…
A Neural-Network-Based Convex Regularizer for Inverse Problems
Alexis Goujon, Sebastian Neumayer, Pakshal Bohra +2
The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in reconstruction quality. Unfortunately, these new methods o…