Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints. Traditional sampling theory suggests that accurate signal reconstruction requires a number of measurements proportional to the signal's ambient dimension, a requirement often too expensive or impractical. Compressed sensing challenges this notion by demonstrating that sparse signals can be recovered with fewer measurements, provided the measurement operator meets certain conditions. This proof-of-concept study presents a task-aware flow-based generative framework -- a reformulation of the conventional Flow Matching training paradigm with a ...
| Authors | Roman Pavelkin, Luis A. Zavala-Mondragon, Christiaan G. A. Viviers et al. |
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| Primary category | cs.CV |
| Published | 22 May 2026 |
| Last updated at source | 11 August 2026 |
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