广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (04): 114-121.doi: 10.12052/gdutxb.230103

• 计算机科学与技术 • 上一篇    下一篇

基于深度学习的自适应采样及记忆增强压缩感知方法

罗成, 张军   

  1. 广东工业大学 信息工程学院 , 广东 广州 510006
  • 收稿日期:2023-08-04 出版日期:2024-07-25 发布日期:2024-08-13
  • 通信作者: 张军(1979–) ，男，教授，博士，主要研究方向为人工智能技术、压缩感知理论及其应用，E-mail：jzhang@gdut.edu.cn
  • 作者简介:罗成(1998–) ，男，硕士研究生，主要研究方向为压缩感知、深度学习等，E-mail：lc20162020@163.com
  • 基金资助:
    国家自然科学基金资助项目(61973088)

Adaptive Sampling and Memory-augmented Compressed Sensing Algorithm Based on Deep Learning

Luo Cheng, Zhang Jun   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-08-04 Online:2024-07-25 Published:2024-08-13

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摘要： 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。

关键词: 深度学习, 自适应采样, 记忆增强

Abstract: 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.

Key words: deep learning, adaptive sampling, memory-augmented

中图分类号: 

  • TP391
[1] DONOHO D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[2] CANDES E J, WAKIN M B. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
[3] LUSTIG M, DONOHO D L, SANTOS J M, et al. Compressed sensing MRI [J]. IEEE Signal Processing Magazine, 2008, 25(2): 72-82.
[4] ENDER J H G. On compressive sensing applied to radar [J]. Signal Processing, 2010, 90(5): 1402-1414.
[5] ZHENG S, CHEN J, ZHANG X P, et al. A new multihypothesis-based compressed video sensing reconstruction system [J]. IEEE Transactions on Multimedia, 2021, 23: 3577-3589.
[6] KULKARNI K, LOHIT S, TURAGA P, et al. Reconnet: non-iterative reconstruction of images from compressively sensed measurements[C]// IEEE Conference on Computer Vision & Pattern Recognition (CVPR) . Las Vegas: IEEE, 2016: 449-458.
[7] SHI W Z, JIANG F, LIU S H, et al. Image compressed sensing using convolutional neural network [J]. IEEE Transactions on Image Processing, 2019, 29: 375-388.
[8] SHI W Z, JIANG F, LIU S H, et al. Scalable convolutional neural network for image compressed sensing[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Long Beach: IEEE, 2019: 12282-12291.
[9] BLUMENSATH T, DAVIES M E. Iterative hard thresholding for compressed sensing [J]. Applied and Computational Harmonic Analysis, 2009, 27(3): 265-274.
[10] ZHANG J, GHANEM B. Istanet: interpretable optimization-inspired deep network for image compressive sensing[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . SaltLake City: IEEE, 2018: 1828-1837.
[11] YOU D, XIE J F, ZHANG J. Ista-net++: flexible deep unfoldingnetwork for compressive sensing[C]// IEEE International Conference on Multimedia and Expo (ICME) . Shenzhen: IEEE, 2021: 1-6.
[12] YU Y, WANG B, ZHANG L M. Saliency-based compressive sampling for image signals [J]. IEEE Signal Processing Letters, 2010, 17(11): 973-976.
[13] ZHOU S W, HE Y, LIU Y H, et al. Multi-channel deep networksfor block-based image compressive sensing [J]. IEEE Transactionson Multimedia, 2021, 23: 2627-2640.
[14] CANDES E J. The restricted isometry property and its implications for compressed sensing [J]. Comptes Rendus Mathematique, 2008, 346: 589-592.
[15] QIU C X, YUE T, HU X M. Adaptive and cascaded compressivesensing[EB/OL]. arXiv: 2203.10779 (2022-3-21) [2023-8-3]. https://doi.org/10.48550/arXiv.2203.10779.
[16] DONOHO D L, MALEKI A, MONTANARI A. Message-passing algorithms for compressed sensing [J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(45): 18914-18919.
[17] LI C, YIN W, JIANG H, et al. An efficient augmented lagrangian method with applications to total variation minimization [J]. Computational Optimization and Applications, 2013, 56(3): 507-530.
[18] ZHANG J, ZHAO C, GAO W. Optimization-inspired compact deep compressive sensing [J]. IEEE Journal of Selected Topics in Signal Processing, 2020, 14(4): 765-774.
[19] ZHANG Z H, LIU Y P, LIU J, et al. Ampnet: denoising-based deep unfolding for compressive image sensing [J]. IEEE Transactions on Image Processing, 2021, 30: 1487-1500.
[20] YOU D, ZHANG J, XIE J F, et al. Coast: controllable arbitrary sampling network for compressive sensing [J]. IEEE Transactions on Image Processing, 2021, 30: 6066-6080.
[21] CHEN W J, YANG C L, YANG X. Fsoinet: feature-space optimi-zation-inspired network for image compressive sensing[C]// IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore: IEEE, 2022: 2460-2464.
[22] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutionalnetworks for biomedical image segmentation[C]// International Conference on Medical Image Computing and Computer Assisted Intervention. Munich: Springer, 2015: 234-241.
[23] LI X, WANG W, HU X, et al. Selective kernel networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. LongBeach: IEEE, 2019: 510-519.
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