融合注意力机制的卷积网络单像素成像

王翔; 周义深; 张轩阁; 陈希浩

doi:10.7498/aps.74.20250010

摘要

提出了一种基于物理驱动的融合注意力机制的新型卷积网络单像素成像方法. 通过将结合通道与空间注意力机制的模块集成到一个随机初始化的卷积网络中, 利用单像素成像的物理模型约束网络, 实现了高质量的图像重建. 具体来说, 将空间与通道两个维度的注意力机制集成为一个模块, 引入到多尺度U-net卷积网络的各层中, 通过这种方式, 不仅可以利用注意力机制在三维数据立方中提供的关键权重信息, 还充分结合了U-net网络在不同空间频率下强大的特征提取能力. 这一创新方法能够有效捕捉图像细节, 抑制背景噪声, 提升图像重建质量. 实验结果表明, 针对低采样率条件下的图像重建, 与传统非预训练网络相比, 融合注意力机制的方案不仅在直观上图像细节重建得更好, 而且在定量的评价指标(如峰值信噪比和结构相似性)上均表现出显著优势, 验证了其在单像素成像中的有效性与应用前景.

关键词:

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.

Keywords:

作者及机构信息

辽宁大学物理学院, 沈阳　110036

通信作者: 陈希浩, xi-haochen@163.com

基金项目: 国家重点研发计划(批准号: 2018YFB0504302)资助的课题.

Authors and contacts

School of Physics, Liaoning University, Shenyang 110036, China

Corresponding author: CHEN Xihao, xi-haochen@163.com

Funds: Project supported by the National Key Research and Development Program of China (Grant No. 2018YFB0504302).

文章全文

参考文献

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施引文献

图 1 实验方案图

Fig. 1. Experimental schematic diagram

下载: 全尺寸图片幻灯片

图 2 融合注意力机制的U-net卷积神经网络结构示意图　(a) U-net结构的卷积网络; (b) CBAM模块结构总览; (c)空间注意力机制模块; (d)通道注意力机制模块

Fig. 2. Schematic diagram of U-net convolutional neural network structure with integrated attention mechanism: (a) Convolutional neural networks of a U-net architecture; (b) overall structure of CBAM; (c) spatial attention module; (d) channel attention module

下载: 全尺寸图片幻灯片

图 3 融合注意力机制与原始U-net网络重建方案在不同采样率下的结果

Fig. 3. Results of the fusion attention mechanism and the original U-net reconstruction scheme under different sampling rates

下载: 全尺寸图片幻灯片

图 4 不同采样率下SSIM的对比

Fig. 4. Comparison of SSIM at different sampling rates

下载: 全尺寸图片幻灯片

图 5 不同采样率下PSNR的对比

Fig. 5. Comparison of PSNR at different sampling rates

下载: 全尺寸图片幻灯片

图 6 不同迭代次数下PSNR与损失函数的变化对比　(a)两种方案重建图像的PSNR随迭代次数的变化; (b)本文方案的损失函数在不同初始学习率下随迭代次数的变化

Fig. 6. Comparison of PSNR and loss function under different iterations: (a) The PSNR of the reconstructed images of the two schemes varies with iterations; (b) the loss function of our scheme varies with iterations under different initial learning rates.

下载: 全尺寸图片幻灯片

下载: 全尺寸图片幻灯片

[1]	Kilcullen P, Ozaki T, Liang J 2022 Nat. Commun. 13 7879 Google Scholar
[2]	Hahamovich E, Monin S, Hazan Y, Rosenthal A 2021 Nat. Commun. 12 4516 Google Scholar
[3]	Shapiro J H 2008 Phys. Rev. A 78 061802 Google Scholar
[4]	Ferri F, Magatti D, Gatti A, Bache M, Brambilla E, Lugiato L 2005 Phys. Rev. Lett. 94 183602 Google Scholar
[5]	Wang F, Wang C, Deng C, Han S, Situ G 2022 Photon. Res. 10 104 Google Scholar
[6]	Pan L, Shen Y, Qi J, Shi J, Feng X 2023 Opt. Express 31 13943 Google Scholar
[7]	Song K, Bian Y, Wang D, Li R, Wu K, Liu H, Qin C, Hu J, Xiao L 2024 Laser & Photonics Rev. published online 2401397
[8]	Zhao X S, Yu C, Wang C, Li T, Liu B, Lu H, Zhang R, Dou X, Zhang J, Pan J W 2024 Appl. Phys. Lett. 125 211103 Google Scholar
[9]	Karpowicz N, Zhong H, Xu J, Lin K I, Hwang J S, Zhang X C 2005 Semicond. Sci. Tech. 20 S293 Google Scholar
[10]	Simões M, Vaz P, Cortez A F V 2024. arXiv: 2411.03907 [physics.ins-det]
[11]	Shwartz S 2021 Sci. Bull. 66 857 Google Scholar
[12]	Olbinado M P, Paganin D M, Cheng Y, Rack A 2021 Optica 8 1538 Google Scholar
[13]	Clemente P, Durán V, Tajahuerce E, Andrés P, Climent V, Lancis J 2013 Opt. Lett. 38 2524 Google Scholar
[14]	Jiang W, Yin Y, Jiao J, Zhao X, Sun B 2022 Photon. Res. 10 2157 Google Scholar
[15]	Gibson G M, Sun B, Edgar M P, Phillips D B, Hempler N, Maker G T, Malcolm G P A, Padgett M J 2017 Opt. Express 25 2998 Google Scholar
[16]	Zhou L, Xiao Y, Chen W 2023 Opt. Express 31 23027 Google Scholar
[17]	Xu Y, Lu L, Saragadam V, Kelly K F 2024 Nat. Commun. 15 1456 Google Scholar
[18]	Li J, Li X, Yardimci N T, Hu J, Li Y, Chen J, Hung Y C, Jarrahi M, Ozcan A 2023 Nat. Commun. 14 6791 Google Scholar
[19]	Li S, Liu X, Xiao Y, Ma Y, Yang J, Zhu K, Tian X 2023 Opt. Express 31 4712 Google Scholar
[20]	Zheng P, Dai Q, Li Z, Ye Z, Xiong J, Liu H C, Zheng G, Zhang S 2021 Sci. Adv. 7 eabg0363 Google Scholar
[21]	Katz O, Bromberg Y, Silberberg Y 2009 Appl. Phys. Lett. 95 131110 Google Scholar
[22]	López-García L, Cruz-Santos W, GarcíaArellano A, Filio-Aguilar P, Cisneros-Martínez J A, Ramos-García R 2022 Opt. Express 30 13714 Google Scholar
[23]	Zhang Z, Ma X, Zhong J 2015 Nat. Commun. 6 6225 Google Scholar
[24]	Donoho D 2006 IEEE Trans. Inf. Theory 52 1289 Google Scholar
[25]	Duarte M F, Davenport M A, Takhar D, Laska J N, Sun T, Kelly K F, Baraniuk R G 2008 IEEE Signal Process Mag. 25 83 Google Scholar
[26]	Huang L, Luo R, Liu X, Hao X 2022 Light Sci. Appl. 11 61 Google Scholar
[27]	Figueiredo M A T, Nowak R D, Wright S J 2007 IEEE J. Sel. Top. Signal Process. 11 586
[28]	Pioneers A 2024 Nat. Mach. Intell. 6 1271 Google Scholar
[29]	查文舒, 李道伦, 沈路航, 张雯, 刘旭亮 2022 力学学报 54 543 Google Scholar Zha W S, Li D L, Shen L H, Zhang W, Liu X L 2022 Chinese Journal of Theoretical and Applied Mechanics 54 543 Google Scholar
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[31]	van Leeuwen C, Podareanu D, Codreanu V, Cai M X, Berg A, Zwart S P, Stoffer R, Veerman M, van Heerwaarden C, Otten S, Caron S, Geng C, Ambrosetti F, Bonvin A M J J 2020 arXiv: 2004.03454[cs.CE]
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[35]	Lyu M, Wang W, Wang H, Wang H, Li G, Chen N, Situ G 2017 Sci. Rep. 7 17865 Google Scholar
[36]	Zhang X, Deng C, Wang C, Wang F, Situ G 2023 ACS Photonics 10 2363 Google Scholar
[37]	Li J, Li Y, Li J, Zhang Q, Li J 2020 Opt. Express 28 22992 Google Scholar
[38]	Wang F, Wang C, Chen M, Gong W, Zhang Y, Han S, Situ G 2022 Light Sci. Appl. 11 1 Google Scholar
[39]	Peng L, Xie S, Qin T, Cao L, Bian L 2023 Opt. Lett. 48 2527 Google Scholar
[40]	Liu H, Bian L, Zhang J 2023 Opt. Laser Technol. 157 108600 Google Scholar
[41]	Liu X, Han T, Zhou C, Huang J, Ju M, Xu B, Song L 2023 Opt. Express 31 9945 Google Scholar
[42]	Hammernik K, Küstner T, Yaman B, Huang Z, Rueckert D, Knoll F, Akçakaya M 2023 IEEE Signal Process Mag. 40 98
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[44]	Ulyanov D, Vedaldi A, Lempitsky V 2020 IJCV 128 1867 Google Scholar
[45]	Ren W, Nie X, Peng T, Scully M O 2022 Opt. Express 30 47921 Google Scholar
[46]	Zhang H, Sindagi V, Patel V M 2020 IEEE Trans. Circuits Syst. Video Technol. 30 3943 Google Scholar
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[48]	Zhang H, Wang Z, Liu D 2014 IEEE Transactions on Neural Networks and Learning Systems 25 1229 Google Scholar
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[50]	Karim N, Rahnavard N 2021 arXiv: 2107.01330[cs.CV]
[51]	Hoshi I, Shimobaba T, Kakue T, Ito T 2020 Opt. Express 28 34069 Google Scholar
[52]	Stollenga M, Masci J, Gomez F, Schmidhuber J 2014 arXiv: 1407.3068[cs.CV]
[53]	Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y 2018 arXiv: 1807.02758[cs.CV]
[54]	Liao X, He L, Mao J, Xu M 2024 Remote Sensing 16 1688 Google Scholar
[55]	Yu W K, Wang S F, Shang K Q 2024 Sensors 24 1012 Google Scholar
[56]	Ronneberger O, Fischer P, Brox T 2015 arXiv: 1505.04597[cs.CV]
[57]	Meng Z, Yu Z, Xu K, Yuan X 2021 arXiv: 2108.12654 [eess.IV]
[58]	Ferri F, Magatti D, Lugiato L A, Gatti A 2010 Phys. Rev. Lett. 104 253603 Google Scholar
[59]	Lin J, Yan Q, Lu S, Zheng Y, Sun S, Wei Z 2022 Photonics 9 343 Google Scholar

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[20]	曹蓓, 罗秀娟, 司庆丹, 曾志红. 相干场成像四光束相位闭合算法研究. 物理学报, 2015, 64(5): 054204. doi: 10.7498/aps.64.054204

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融合注意力机制的卷积网络单像素成像