arXiv:2509.15026v2 Announce Type: replace-cross Abstract: We seek to recover an unknown signal from nonlinear amplitude-only measurements, a challenging inverse problem. Strong theoretical guarantees have been established for idealized random measurements, defining the sampling ratio required for signal recovery. However, these results neglect signal priors, which can fundamentally shift these limits, potentially enabling reconstruction with far fewer measurements and simpler models. We evaluate a variety of image priors in the context of severe undersampling with physically-grounded random me
This is a technical research paper published on arXiv, representing incremental academic progress in a specific area of AI.
While contributing to the theoretical foundation of signal processing and machine learning, this specific paper does not have immediate strategic implications for broader economic or geopolitical considerations.
This paper offers an incremental improvement in signal recovery methods using learned regularizers, potentially leading to more efficient AI image processing down the line.
Improved theoretical understanding of random phase retrieval in AI.
Potential for more robust and efficient image reconstruction algorithms in scientific and medical imaging applications over many years.
Future AI systems might benefit from these types of foundational advances, enabling better performance with less data or under challenging conditions.
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