物理学报2025，Vol.74Issue(8)：86-95,10.DOI:10.7498/aps.74.20250010
融合注意力机制的卷积网络单像素成像
Convolutional network single-pixel imaging with fusion attention mechanism
摘要
Abstract
This paper presents a novel convolutional neural network-based single-pixel imaging method that integrates a physics-driven fusion attention mechanism.By incorporating a module that combines both channel attention mechanism and spatial attention mechanism into a randomly initialized convolutional network,the method utilizes the physical model constraints of single-pixel imaging to achieve high-quality image reconstruction.Specifically,the spatial and channel attention mechanism are combined into a single module and introduced into various layers of a multi-scale U-net convolutional network.In the spatial attention mechanism,we extract the attention weight features of each spatial region of the pooled feature map by using convolution.In the channel attention mechanism,we pool the three-dimensional feature map into a single-channel signal and input it into a two-layer fully connected network to obtain the attention weight information for each channel.This approach not only uses the critical weighting information provided by the attention mechanism in the three-dimensional data cube but also fully integrates the powerful feature extraction capabilities of the U-net network across different spatial frequencies.This innovative method can effectively capture image details,suppress background noise,and improve image reconstruction quality.During the experimental phase,we employ the optical path of single-pixel imaging to acquire bucket signals for two target images,"snowflake"and"basket".By inputting any noisy image into a randomly initialized neural network with attention mechanism,and using the mean square error between simulated bucket signal and actual bucket signal,we physically constrain the convergence of the network.Ultimately,we achieve a reconstructed image that adheres to the physical model.The experimental results demonstrate that under low sampling rate conditions,the scheme of integrating the attention mechanism can not only intuitively reconstruct image details better,but also demonstrate significant advantages in quantitative evaluation metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM),confirming its effectiveness and potential application in single-pixel imaging.关键词
单像素成像/注意力机制/卷积神经网络/图像重构Key words
single-pixel imaging/attention mechanisms/convolutional neural networks/image reconstruction引用本文复制引用
王翔,周义深,张轩阁,陈希浩..融合注意力机制的卷积网络单像素成像[J].物理学报,2025,74(8):86-95,10.基金项目
国家重点研发计划(批准号:2018YFB0504302)资助的课题. Project supported by the National Key Research and Development Program of China(Grant No.2018YFB0504302). （批准号:2018YFB0504302）
