Deep Probabilistic Unfolding for Quantized Compressive Sensing

TL;DR

This paper introduces a deep probabilistic unfolding model for quantized compressive sensing that improves reconstruction accuracy by respecting quantization physics and capturing multi-scale features.

Contribution

It develops a novel unfolding framework with a stable likelihood gradient projection and a dual-domain Mamba module for enhanced reconstruction.

Findings

Achieves state-of-the-art performance in quantized compressive sensing tasks.

Effectively captures multi-scale local and global features.

Demonstrates practical applicability in real-world scenarios.

Abstract

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.