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Memory-efficient deep end-to-end posterior network (DEEPEN) for inverse problems
The CNN weights are learned from training data in an E2E fashion using maximum likelihood optimization.
Local monotone operator learning using non-monotone operators: MnM-MOL
Inspired by convex-non-convex regularization strategies, we now impose the monotone constraint on the sum of the gradient of the data term and the CNN block, rather than constrain the CNN itself to be a monotone operator.
Multi-Scale Energy (MuSE) plug and play framework for inverse problems
We introduce a multi-scale optimization strategy, where a sequence of smooth approximations of the true prior is used in the optimization process.
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