Learned Adaptive Multiresolution Diffusion Imaging
Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are usually governed by fixed local criteria. We introduce Learned Adaptive Multiresolution Diffusion Imaging (Learned AMDI), which preserves the AMDI fixed-tree propagator and hierarchy constraints while replacing the post-propagation selector with a shared local policy trained by proximal policy optimization. Regression tests reproduce deterministic AMDI trajectories to machine precision when identical trees are used. In the Haar implementation studied here, the deterministic one-step selector accepts no refinements in 54 decisions. Across nine ...
| Authors | Christian Tantardini, Stig Rune Jensen, Roberto Di Remigio Eikås et al. |
|---|---|
| Primary category | math.NA |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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