A two-step trained neural network predicts regularization parameters for noisy-operator inverse scattering, yielding faster linear sampling method reconstructions with contrast at or above manually tuned Morozov regularization in synthetic tests.
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Deep regularization networks for inverse problems with noisy operators
A two-step trained neural network predicts regularization parameters for noisy-operator inverse scattering, yielding faster linear sampling method reconstructions with contrast at or above manually tuned Morozov regularization in synthetic tests.