Search Results for author:
Found 9 papers, 3 papers with code
Bayesian Optimization of Sampling Densities in MRI
Data-driven optimization of sampling patterns in MRI has recently received a significant attention. Following recent observations on the combinatorial number of minimizers in off-the-grid optimization, we propose a framework to globally optimize the sampling densities using Bayesian optimization.
Training Adaptive Reconstruction Networks for Blind Inverse Problems
Neural networks allow solving many ill-posed inverse problems with unprecedented performance.
Blind inverse problems with isolated spikes
Assume that an unknown integral operator living in some known subspace is observed indirectly, by evaluating its action on a few Dirac masses at unknown locations.
Off-the-grid data-driven optimization of sampling schemes in MRI
We propose a novel learning based algorithm to generate efficient and physically plausible sampling patterns in MRI.
Fast Wavelet Decomposition of Linear Operators through Product-Convolution Expansions
Wavelet decompositions of integral operators have proven their efficiency in reducing computing times for many problems, ranging from the simulation of waves or fluids to the resolution of inverse problems in imaging.
Hyperspectral pan-sharpening: a variational convex constrained formulation to impose parallel level lines, solved with ADMM
In this paper, we address the issue of hyperspectral pan-sharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral image.
Variable density sampling with continuous trajectories. Application to MRI
Its standard design relies on the random drawing of independent measurements.
Applications
Processing stationary noise: model and parameter selection in variational methods
We recently proposed a variational algorithm to tackle this issue.
Image restoration using sparse approximations of spatially varying blur operators in the wavelet domain
Restoration of images degraded by spatially varying blurs is an issue of increasing importance in the context of photography, satellite or microscopy imaging.
