Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications

ArXi:2606.00078v1 Announce Type: cross 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 nstrating that sparse signals can be recovered with fewer measurements, provided the measurement operator meets certain conditions.