用概率展开方法提升量化压缩感知重建精度与效率
Deep Probabilistic Unfolding for Quantized Compressive Sensing
- 基于概率展开框架，将硬约束转为软概率引导
- 在多个数据集上达到当前最优重建性能
- 适合需要高精度重建的实时压缩感知应用
我们提出一种深度概率展开模型，解决经典量化压缩感知问题。不同于以往使用L2投影的方法，本工作推导出闭式、数值稳定的似然梯度投影，使模型能真实反映量化物理特性，将硬性量化约束转化为软概率指导。此外，设计了一种高效的双域Mamba模块，动态捕捉并融合多尺度局部与全局特征，确保远距离但相关区域间的有效交互。大量实验表明，所提方法在多个基准上优于现有工作，显著提升了量化压缩感知的实际应用潜力。
原文摘要 · Abstract (English)
We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to enhance the reconstruction accuracy and efficiency. Unlike previous unfolding methods that apply L2 projection to measurements, we derive a closed-form, numerically stable likelihood gradient projection, which allows the model to respect the true quantization physics, turning the hard quantization constraint into a soft probabilistic guidance. Furthermore, an efficient, dual-domain Mamba module is specifically designed to dynamically capture and fuse the multi-scale local and global features, ensuring the interactions between the distant but correlated regions. Extensive experiments demonstrate the state-of-the-art performance of the proposed method over previous works, which is capable of promoting the application of quantized compressive sensing in real life.
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