计算机工程与科学2025，Vol.47Issue(3)：485-493,9.DOI:10.3969/j.issn.1007-130X.2025.03.010
基于迭代收缩阈值与深度学习的压缩感知图像重构网络
A compressive sensing image reconstruction network based on iterative shrinkage thresholding and deep learning
摘要
Abstract
Aiming at the problems of low refinement of image reconstruction and weak network gen-eralization ability in compressive sensing reconstruction algorithms based on deep learning,a compres-sive sensing image reconstruction network(EH-ISTANet)based on iterative shrinkage thresholding and deep learning is proposed.The model consists of three parts:extraction subnetwork,initialization sub-network and enhancement reconstruction subnetwork.It adds the attention mechanism and cooperates with the neighborhood mapping module to send the obtained features to the enhancement module,so as to enhance the edge and texture of the reconstructed image.The reconstruction stage mimics the unfol-ding process of the traditional iterative shrinkage thresholding algorithm,and each stage can flexibly model the measurement matrix and dynamically adjust the step size in the gradient descent step.It is verified that the peak signal-to-noise ratio of the model is improved in different datasets with different sampling rates.It is demonstrated that the model outperforms other models in improving generalization ability and reconstruction accuracy.When the compressive sensing rate is 10％,the average signal-to-noise ratio of this model on five testsets is improved by 1.69 dB,4.36 dB and 1.93 dB compared with CSNet,AMP-Net,and AMP-Net-BM models.关键词
压缩感知/深度学习/注意力机制/特征增强Key words
compressive sensing/deep learning/attention mechanism/feature enhancement分类
信息技术与安全科学引用本文复制引用
徐雯,于瓅..基于迭代收缩阈值与深度学习的压缩感知图像重构网络[J].计算机工程与科学,2025,47(3):485-493,9.基金项目
安徽省重点研究与开发计划(202104d07020010) （202104d07020010）
