  Compressed Sensing (CS) facilitates rapid image acquisition by selecting a
small subset of measurements sufficient for high-fidelity reconstruction.
Adaptive CS seeks to further enhance this process by dynamically choosing
future measurements based on information gleaned from data that is already
acquired. However, many existing frameworks are often tailored to specific
tasks and require intricate training procedures. We propose AdaSense, a novel
Adaptive CS approach that leverages zero-shot posterior sampling with
pre-trained diffusion models. By sequentially sampling from the posterior
distribution, we can quantify the uncertainty of each possible future linear
measurement throughout the acquisition process. AdaSense eliminates the need
for additional training and boasts seamless adaptation to diverse domains with
minimal tuning requirements. Our experiments demonstrate the effectiveness of
AdaSense in reconstructing facial images from a small number of measurements.
Furthermore, we apply AdaSense for active acquisition of medical images in the
domains of magnetic resonance imaging (MRI) and computed tomography (CT),
highlighting its potential for tangible real-world acceleration.
