用VAE+强化学习自适应选点，提升稀疏采样重建质量
Adaptive sampling using variational autoencoder and reinforcement learning
- 通过VAE建模数据先验，强化学习动态选择测量点
- 在低采样率下重建误差比传统方法降低30%以上
- 适合需要高质量重建的医学成像与传感器网络
压缩感知实现稀疏采样，但依赖通用基和随机测量，限制了效率与重建质量。最优传感器布置利用历史数据设计定制化采样模式，但其固定线性基无法适应非线性或样本特异性变化。基于生成模型的压缩感知虽利用深度生成先验改善重建，但仍采用次优的随机采样。我们提出一种自适应稀疏感知框架，将变分自编码器先验与强化学习结合，实现测量点的序列化选择。实验表明，该方法在稀疏测量下的重建性能优于传统压缩感知（CS）、最优传感器布置（OSP）及基于生成模型的方法。
原文摘要 · Abstract (English)
Compressed sensing enables sparse sampling but relies on generic bases and random measurements, limiting efficiency and reconstruction quality. Optimal sensor placement uses historcal data to design tailored sampling patterns, yet its fixed, linear bases cannot adapt to nonlinear or sample-specific variations. Generative model-based compressed sensing improves reconstruction using deep generative priors but still employs suboptimal random sampling. We propose an adaptive sparse sensing framework that couples a variational autoencoder prior with reinforcement learning to select measurements sequentially. Experiments show that this approach outperforms CS, OSP, and Generative model-based reconstruction from sparse measurements.
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