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Found 4 papers, 2 papers with code
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
Unsupervised learning is a training approach in the situation where ground truth data is unavailable, such as inverse imaging problems.
Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging
Together with a detailed survey, we provide an overview of the key mathematical results that underlie the methods reviewed in the chapter to keep our discussion self-contained.
Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals
The score term is given in closed form by a regularized Wasserstein proximal, using a kernel convolution that is approximated by sampling.
Provably Convergent Plug-and-Play Quasi-Newton Methods
Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM, with applications in inverse problems and imaging.
