5 papers
From sparse recovery to plug-and-play priors, understanding trade-offs for stable recovery with generalized projected gradient descent
Ali Joundi, Yann Traonmilin, Jean-François Aujol
We consider the problem of recovering an unknown low-dimensional vector from noisy, underdetermined observations. We focus on the Generalized Projected Gradient Descent (GPGD) fram…
A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm
Oscar Leong, Yann Traonmilin
Recovering high-dimensional signals from corrupted measurements is a central challenge in inverse problems. Recent advances in generative diffusion models have shown remarkable emp…
Stochastic Orthogonal Regularization for deep projective priors
Ali Joundi, Yann Traonmilin, Alasdair Newson
Many crucial tasks of image processing and computer vision are formulated as inverse problems. Thus, it is of great importance to design fast and robust algorithms to solve these p…
Parameter-free structure-texture image decomposition by unrolling
Laura Girometti, Jean-François Aujol, Antoine Guennec +1
In this work, we propose a parameter-free and efficient method to tackle the structure-texture image decomposition problem. In particular, we present a neural network LPR-NET based…
On the impact of the parametrization of deep convolutional neural networks on post-training quantization
Samy Houache, Jean François Aujol, Yann Traonmilin
This paper introduces novel theoretical approximation bounds for the output of quantized neural networks, with a focus on convolutional neural networks (CNN). By considering layerw…