Journal of Guangdong University of Technology ›› 2024, Vol. 41 ›› Issue (04): 114-121.doi: 10.12052/gdutxb.230103

• Computer Science and Technology • Previous Articles     Next Articles

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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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

CLC Number: 

  • 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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