A prox-based semi-smooth Newton method for TV-minimization that is globally well-posed and locally superlinearly convergent under finite element discretization, extending to broader convex problems.
IEEE Transactions on Image Processing , year = 2009, month = nov, volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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A visual transformer model trained on IRIS inversions predicts chromospheric temperature and density from SDO data with correlations around 0.8 on 80% of test cases.
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A $\operatorname{prox}$-Based Semi-Smooth Newton Method for TV-Minimization
A prox-based semi-smooth Newton method for TV-minimization that is globally well-posed and locally superlinearly convergent under finite element discretization, extending to broader convex problems.
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Predicting the thermodynamics in the chromosphere from the translation of SDO data into the IRIS$^{2}$ inversion results using a visual transformer model
A visual transformer model trained on IRIS inversions predicts chromospheric temperature and density from SDO data with correlations around 0.8 on 80% of test cases.