A Markov-modeled maximum likelihood estimator for multi-look holographic reconstruction achieves near-ideal performance under strong inter-look speckle correlation by outperforming methods that assume independence.
Plug-and-play priors for model based reconstruction
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Reinforcement learning optimizes adaptive angle selection and dose allocation in sparse-view CT reconstruction, yielding better quality and defect detectability than uniform strategies under limited projections or dose.
citing papers explorer
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Maximum Likelihood Reconstruction for Multi-Look Digital Holography with Markov-Modeled Speckle Correlation
A Markov-modeled maximum likelihood estimator for multi-look holographic reconstruction achieves near-ideal performance under strong inter-look speckle correlation by outperforming methods that assume independence.
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Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction
Reinforcement learning optimizes adaptive angle selection and dose allocation in sparse-view CT reconstruction, yielding better quality and defect detectability than uniform strategies under limited projections or dose.