Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
Quick Take
This study introduces a task-aware flow-based generative framework that optimizes subsampling in compressed sensing, achieving state-of-the-art Peak Signal-to-Noise Ratios of 25.17 dB at 5% subsampling on CelebA and 29.24 dB for 8× accelerated MRI on fastMRI, enhancing image classification and reconstruction performance.
Key Points
- Proposes a flow-based generative model for optimizing subsampling in compressed sensing.
- Achieves 25.17 dB PSNR at 5% subsampling on CelebA dataset.
- Demonstrates 29.24 dB PSNR for 8× accelerated MRI on fastMRI dataset.
- Enhances performance for image classification and reconstruction tasks.
- Offers a flexible approach for data-driven sensing schemes.
Article Content
From source RSS / original summaryarXiv:2606. 00078v1 Announce Type: new Abstract: 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 flow model trained to optimize subsampling in compressed sensing applications.
We establish the fundamental feasibility of the proposed framework of learning subsampling masks that substantially enhance the performance of compressed sensing for image classification, image reconstruction, and MRI acceleration. For the image reconstruction task, our method demonstrated state-of-the-art performance, achieving Peak Signal-to-Noise Ratio of 25. 17 dB at the subsampling rate of 5\% on the CelebA dataset and 29.
24 dB when reconstructing $8\times$ accelerated MRI measurements (fastMRI dataset) with the minimal computational overhead. These results highlight the effectiveness of task-conditioning within generative flow models and reveal a promising direction for representation learning strategies. Overall, the proposed framework offers a unified, flexible approach to designing data- and task-driven sensing schemes that can be potentially adapted to a broad range of inverse problems.
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