mlsanityData to 7 Oct 2026

Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling

arXiv 2407.08256 ↗First posted 11 Jul 2024eess.IV cs.CV cs.LGcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 54 of its 81 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

54 of 81 references.

References · 54

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
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38 more
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  14. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
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  24. 2019Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisitioncited by 55
  25. 2018Compressed Sensing with Deep Image Prior and Learned Regularizationcited by 47
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 6.

12 papers.

  1. 2025SILO: Solving Inverse Problems with Latent Operatorscited by 18
  2. 2025InvFusion: Bridging Supervised and Zero-shot Diffusion for Inverse Problemscited by 9
  3. 2024PSC: Posterior Sampling-Based Compressioncited by 5
  4. 2025Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomographycited by 4
  5. 2025Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stoppingcited by 1
  6. 2025Sampling Innovation-Based Adaptive Compressive Sensingcited by 1
  7. 2026NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Trainingcited by 1
  8. 2026Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisitioncited by 0
  9. 2026Stage-wise Distortion-Perception Traversal in Zero-shot Inverse Problems with Diffusion Modelscited by 0
  10. 2026Classification-oriented adaptive sensing via posterior samplingcited by 0
  11. 2026How Many Posterior Samples? Calibrated Stopping for Adaptive Sensingcited by 0
  12. 2026GPARA: Graph-Posterior-Aligned Refinement and Active Acquisition for Grounding Diffusion Priorscited by 0

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  1. 2024Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
  2. 2025Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
  3. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  4. 2026U-DAVI: Uncertainty-Aware Diffusion-Prior-Based Amortized Variational Inference for Image Reconstruction
  5. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction
  6. 2025Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection
  7. 2024Amortized Posterior Sampling with Diffusion Prior Distillation
  8. 2024Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements