Feng Mingkun, Ning Dongcan, Yang Shengying
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel). 2024 Dec 18;24(24):8085. doi: 10.3390/s24248085.
Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS). Concretely, without loss of interpretability, we integrate an adaptive content-aware strategy into the gradient descent step of the proximal gradient descent (PGD) algorithm, driving it to adaptively capture the adequate features. In addition, we extended AMAUN-CS based on the memory storage mechanism of the human brain and propose AMAUN-CS to develop the dependency of deep information across cascading stages. The experimental results show that the AMAUN-CS model surpasses other advanced methods on various public benchmark datasets while having lower complexity in training.
深度展开网络（DUNs）因其良好的可解释性和高性能，在压缩感知（CS）中受到越来越多的关注。然而，许多深度展开网络常常以大量参数为代价来提高重建效果，并且在迭代过程中存在特征信息丢失的问题。本文提出了一种用于压缩感知的新型自适应记忆增强展开网络（AMAUN-CS）。具体而言，在不损失可解释性的情况下，我们将一种自适应内容感知策略集成到近端梯度下降（PGD）算法的梯度下降步骤中，促使其自适应地捕获足够的特征。此外，我们基于人类大脑的记忆存储机制对AMAUN-CS进行扩展，并提出了AMAUN-CS以发展跨级联阶段的深度信息依赖性。实验结果表明，AMAUN-CS模型在各种公共基准数据集上超越了其他先进方法，同时在训练中具有更低的复杂度。
