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Found 5 papers, 3 papers with code
Explainable bilevel optimization: an application to the Helsinki deblur challenge
In this paper we present a bilevel optimization scheme for the solution of a general image deblurring problem, in which a parametric variational-like approach is encapsulated within a machine learning scheme to provide a high quality reconstructed image with automatically learned parameters.
DCT-Former: Efficient Self-Attention with Discrete Cosine Transform
Since their introduction the Trasformer architectures emerged as the dominating architectures for both natural language processing and, more recently, computer vision applications.
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography
We propose a novel convolutional neural network (CNN), called $\Psi$DONet, designed for learning pseudodifferential operators ($\Psi$DOs) in the context of linear inverse problems.
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution.
A scaled gradient projection method for Bayesian learning in dynamical systems
If the unknown impulse response is modeled as a Gaussian process with a suitable kernel, the maximization of the marginal likelihood leads to a challenging nonconvex optimization problem, which requires a stable and effective solution strategy.
