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Beyond Wiener's Lemma: Nuclear Convolution Algebras and the Inversion of Digital Filters
Julien Fageot, Michael Unser, John Paul Ward
A convolution algebra is a topological vector space that is closed under the convolution operation. It is said to be inverse-closed if each element of w…
Imaging thick samples with optical tomography
Alexandre Goy, Morteza H. Shoreh, JooWon Lim +2
Optical diffraction tomography (ODT), initially described in the seminal paper of Emil Wolf [Opt. Comm., 1(4), 153-156 (1969)], has received renewed attention recently. The vast ma…
Proximity Operators for Phase Retrieval
Ferréol Soulez, Éric Thiébaut, Antony Schutz +3
We present a new formulation of a family of proximity operators that generalize the projector step for phase retrieval. These proximity operators for noisy intensity measurements c…
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
Michael T. McCann, Kyong Hwan Jin, Michael Unser
In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large…
Angular Accuracy of Steerable Feature Detectors
Zsuzsanna Püspöki, Arash Amini, Julien Fageot +2
The detection of landmarks or patterns is of interest for extracting features in biological images. Hence, algorithms for finding these keypoints have been extensively investigated…
Compactly-Supported Smooth Interpolators for Shape Modeling with Varying Resolution
Daniel Schmitter, Julien Fageot, Anaïs Badoual +2
In applications that involve interactive curve and surface modeling, the intuitive manipulation of shapes is crucial. For instance, user interaction is facilitated if a geometrical…