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

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。

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 rules 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.