Learned iterative methods based on gradient descent, Gauss-Newton, and Quasi-Newton updates are applied to quantitative photoacoustic tomography, showing improved generalization on simulated and digital twin data with scarce training data and modeling errors.
Deep-plug-and- play proximal gauss-newton method with applications to nonlinear, ill- posed inverse problems,
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Towards robust quantitative photoacoustic tomography via learned iterative methods
Learned iterative methods based on gradient descent, Gauss-Newton, and Quasi-Newton updates are applied to quantitative photoacoustic tomography, showing improved generalization on simulated and digital twin data with scarce training data and modeling errors.