基于深度学习的自适应采样及记忆增强压缩感知方法

罗成; 张军

doi:10.12052/gdutxb.230103

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基于深度学习的自适应采样及记忆增强压缩感知方法

计算机科学与技术 | 更新时间：2026-05-27

- 基于深度学习的自适应采样及记忆增强压缩感知方法
- Adaptive Sampling and Memory-augmented Compressed Sensing Algorithm Based on Deep Learning
- 广东工业大学学报 2024年41卷第4期页码：114-121
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
  
  罗成(1998–) ，男，硕士研究生，主要研究方向为压缩感知、深度学习等，E-mail：lc20162020@163.com
  张军(1979–) ，男，教授，博士，主要研究方向为人工智能技术、压缩感知理论及其应用，E-mail：jzhang@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61973088)
- DOI：10.12052/gdutxb.230103
  中图分类号： TP391
- 纸质出版：2024
- 稿件说明：
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罗成, 张军. 基于深度学习的自适应采样及记忆增强压缩感知方法[J]. 广东工业大学学报, 2024, 41(4): 114-121.

Luo Cheng, Zhang Jun. Adaptive Sampling and Memory-augmented Compressed Sensing Algorithm Based on Deep Learning[J]. 2024, 41(4): 114-121.
罗成, 张军. 基于深度学习的自适应采样及记忆增强压缩感知方法[J]. 广东工业大学学报, 2024, 41(4): 114-121. DOI： 10.12052/gdutxb.230103.

Luo Cheng, Zhang Jun. Adaptive Sampling and Memory-augmented Compressed Sensing Algorithm Based on Deep Learning[J]. 2024, 41(4): 114-121. DOI： 10.12052/gdutxb.230103.

摘要

深度学习技术在压缩感知重构中的应用显著提高了重构的速度和精度。然而现有的深度压缩感知算法通常采用相同的采样率来处理不同的块，忽视了不同图像块具有不同重构难度的事实。本文提出了一种自适应采样与记忆增强的压缩感知算法。首先，本文基于测量域的重构误差估计不同块的重构难度，然后设计规则来自适应分配采样率，采样矩阵则通过掩码实现在特定采样率下的图像块采样。进一步地，在重构网络中增加双支路融合模块来增强上下文记忆的交互，通过调整不同记忆支路的通道权重，提高了网络的重构能力。实验结果表明：与其他方法相比，所提出的算法在几个常用数据集上的平均结构相似性(Structural Similarity

SSIM) 提高了

0.0269

，平均峰值信噪比(Peak Signal-to-Noise Ratio

PSNR) 提高了1.66 dB。

Abstract

The deep learning technology has significantly improved the speed and accuracy of compressed sensing reconstruction. However

the existing deep compressive sensing algorithms usually use the same sampling rate to process different blocks of an image

ignoring the fact that different image blocks have different reconstruction difficulties. In this paper

a compressive sensing algorithm with adaptive sampling and memory enhancement is proposed. Firstly

the reconstruction difficulty of different blocks is estimated based on the reconstruction error of the measurement domain. Then

the ru

les are designed to adaptively assign the sampling rates

and the sampling matrix is used to sample each image block at a specific sampling rate with the help of a sampling rate mask. Furthermore

the two-branch aggregation module is added to the reconstruction network to enhance the interaction of context memory

and the reconstruction ability of the network is improved by adjusting the channel weight of different memory branches. The experimental results show that the proposed algorithm increases the average SSIM by approximately

0.0269

and the average PSNR by approximately 1.66 dB over other methods on several common datasets.

关键词

Keywords

references

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