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Adaptive Memory-Augmented Unfolding Network for Compressed Sensing.

Mingkun Feng¹, Dongcan Ning¹, Shengying Yang¹

¹School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

This study introduces an adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS). The novel approach enhances feature capture and information dependency, outperforming existing methods with lower training complexity.

Keywords:

compressed sensing deep unrolling image reconstruction neural networks proximal gradient descent

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Area of Science:

Signal Processing
Machine Learning
Computer Vision

Background:

Deep unfolding networks (DUNs) are popular for compressed sensing (CS) due to interpretability and performance.
Existing DUNs often suffer from high parameter counts and feature information loss during iterations.

Purpose of the Study:

To propose a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).
To address limitations of current DUNs, specifically parameter count and feature loss.

Main Methods:

Integration of an adaptive content-aware strategy into the proximal gradient descent (PGD) algorithm.
Extension of AMAUN-CS to AMAUN-CS+ incorporating a memory storage mechanism for cross-stage information dependency.

Main Results:

AMAUN-CS adaptively captures adequate features without losing interpretability.
AMAUN-CS+ effectively develops deep information dependency across cascading stages.
AMAUN-CS model surpasses advanced methods on benchmark datasets.

Conclusions:

AMAUN-CS offers improved performance in compressed sensing.
The proposed network achieves superior results with lower training complexity compared to existing methods.