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Found 6 papers, 1 papers with code
Plug-and-play imaging with model uncertainty quantification in radio astronomy
In a previous work, we introduced a class of convergent PnP algorithms, dubbed AIRI, relying on a forward-backward algorithm, with a differentiable data-fidelity term and dynamic range-specific denoisers trained on highly pre-processed unrelated optical astronomy images.
Equivariant plug-and-play image reconstruction
Plug-and-play algorithms constitute a popular framework for solving inverse imaging problems that rely on the implicit definition of an image prior via a denoiser.
Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers
They are typically trained for a specific task, with a supervised loss to learn a mapping from the observations to the image to recover.
Deep network series for large-scale high-dynamic range imaging
We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input.
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm.
Learning Maximally Monotone Operators for Image Recovery
Recently, several works have proposed to replace the operator related to the regularization by a more sophisticated denoiser.
Automated Theorem Proving
Image Restoration
Optimization and Control
Image and Video Processing
47H05, 90C25, 90C59, 65K10, 49M27, 68T07, 68U10, 94A08
