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Compressed Sensing via Measurement-Conditional Generative Models
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DOI：10.1109/ACCESS.2021.3128721.png)
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Abstract
En 中文
Pre-trained generators have been frequently adopted in compressed sensing (CS) owing to their ability to effectively estimate signals with the prior of NNs. To further refine the NN-based prior, we propose a framework that allows the generator to utilize additional information from given measurements of training samples for prior learning, thereby yielding more accurate reconstruction for signals. As our framework has a simple form, it can be easily applied to existing CS methods using pre-trained generators. Through extensive experiments, we demonstrate that our framework consistently outperforms these works by a large margin and can reduce the reconstruction error up to an order of magnitude for the presented target applications. We also explain the experimental success theoretically by showing that our framework can slightly relax the stringent signal presence condition, which is required to guarantee the success of signal recovery.
Keywords:
Generators
Training
Image reconstruction
Generative adversarial networks
Magnetic resonance imaging
Phase measurement
Artificial neural networks
Compressed sensing
artificial neural networks
image reconstruction
image enhancement
signal reconstruction and prediction
measurement-conditional generative models
mitigation of signal presence condition
magnetic resonance imaging
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W