Optics and Precision Engineering, Volume. 32, Issue 14, 2311(2024)
Iterative reconstruction of compressive sensing combining image hierarchical-feature
[1] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).
[2] DUARTE M F, DAVENPORT M A, TAKHAR D et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 25, 83-91(2008).
[3] ZHU L, WU X, SUN Z Y et al. Compressed-sensing accelerated 3-dimensional magnetic resonance cholangiopancreatography: application in suspected pancreatic diseases[J]. Investigative Radiology, 53, 150-157(2018).
[4] YUAN X, BRADY D J, KATSAGGELOS A K. Snapshot compressive imaging: theory, algorithms, and applications[J]. IEEE Signal Processing Magazine, 38, 65-88(2021).
[5] KUMAR S, MAHADEVAPPA M, DUTTA P K. Compressive holography from poisson noise plagued holograms using expectation-maximization[J]. IEEE Transactions on Computational Imaging, 6, 857-867(2020).
[6] TROCAN M, TRAMEL E W, FOWLER J E et al. Compressed-sensing recovery of multiview image and video sequences using signal prediction[J]. Multimedia Tools and Applications, 72, 95-121(2014).
[7] AFONSO M V, BIOUCAS-DIAS J M, FIGUEIREDO M A T. An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems[J]. IEEE Transactions on Image Processing, 20, 681-695(2011).
[8] METZLER C A, MALEKI A, BARANIUK R G. From denoising to compressed sensing[J]. IEEE Transactions on Information Theory, 62, 5117-5144(2016).
[9] BAYRAM İ. On the convergence of the iterative shrinkage/thresholding algorithm with a weakly convex penalty[J]. IEEE Transactions on Signal Processing, 64, 1597-1608(2016).
[10] KULKARNI K, LOHIT S, TURAGA P et al. ReconNet: non-iterative reconstruction of images from compressively sensed measurements[C], 449-458(2016).
[11] SHI W Z, JIANG F, LIU S H et al. Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 29, 375-388(2020).
[12] [12] 田金鹏,侯保军.基于深度展开自注意力网络的压缩感知图像重构[J/OL]. 吉林大学学报(工学版),1-9. (2023-04-10) [2024-02-27]. http://kns.cnki.net/kcms/detail/22.1341.t.20230407. 1455.005.html.TIANJ P, HOUB J. Compressive sensing image reconstruction based on deep unfolding self-attention network [J/OL]. Journal of Jilin University(Engineering and Technology Edition, 1-9. (2023-04-10) [2024-02-27]. http://kns.cnki.net/kcms/detail/22.1341.t.20230407.1455.005.html.(in Chinese)
[13] ZHANG Z H, LIU Y P, LIU J N et al. AMP-net: denoising-based deep unfolding for compressive image sensing[J]. IEEE Transactions on Image Processing, 30, 1487-1500(2021).
[14] ZHANG J, GHANEM B. ISTA-net: interpretable optimization-inspired deep network for image compressive sensing[C], 1828-1837(2018).
[15] YOU D, XIE J F, ZHANG J. ISTA-NET: flexible deep unfolding network for compressive sensing[C], 1-6(2021).
[16] ZHANG J, ZHAO C, GAO W. Optimization-inspired compact deep compressive sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 14, 765-774(2020).
[17] [17] 陈文俊, 杨春玲. 图像压缩感知的特征域优化及自注意力增强神经网络重构算法[J]. 电子学报, 2022, 50(11): 2629-2637. doi: 10.12263/DZXB.20220155CHENW J, YANGC L. Feature-space optimization-inspired and self-attention enhanced neu? ral network reconstruction algorithm for image compressive sensing[J]. Acta Electronica Sinica, 2022, 50(11): 2629-2637.(in Chinese). doi: 10.12263/DZXB.20220155
[19] WANG X L, GIRSHICK R, GUPTA A et al. Non-local neural networks[C], 7794-7803(2018).
[21] LIU Z, LIN Y T, CAO Y et al. Swin Transformer: hierarchical Vision Transformer using Shifted Windows[C], 9992-10002(2021).
[22] SHEN M H, GAN H P, NING C et al. TransCS: a transformer-based hybrid architecture for image compressed sensing[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 31, 6991-7005(2022).
[23] YE D J, NI Z K, WANG H L et al. CSformer: bridging convolution and transformer for compressive sensing[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 32, 2827-2842(2023).
[24] LI Y W, FAN Y C, XIANG X Y et al. Efficient and explicit modelling of image hierarchies for image restoration[C], 18278-18289(2023).
[25] GUO M H, XU T X, LIU J J et al. Attention mechanisms in computer vision: a survey[J]. Computational Visual Media, 8, 331-368(2022).
[26] ARBELÁEZ P, MAIRE M, FOWLKES C et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 898-916(2011).
[27] ZHANG J, ZHAO D B, GAO W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 23, 3336-3351(2014).
[28] MARTIN D, FOWLKES C et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C], 416-423(2001).
[29] HUANG J B, SINGH A, AHUJA N. Single image super-resolution from transformed self-exemplars[C], 5197-5206(2015).
[30] KINGMA D P, BA J. Adam: a method for stochastic optimization[J]. ArXiv e-Prints(2014).
[31] LOSHCHILOV I, HUTTER F. SGDR: stochastic gradient descent with warm restarts[J]. ArXiv e-Prints(2016).
[32] WANG Z, BOVIK A C, SHEIKH H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 13, 600-612(2004).
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Yuhong LIU, Heng YANG. Iterative reconstruction of compressive sensing combining image hierarchical-feature[J]. Optics and Precision Engineering, 2024, 32(14): 2311
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Received: Mar. 1, 2024
Accepted: --
Published Online: Sep. 27, 2024
The Author Email: Heng YANG (11220697@stu.lzjtu.edu.cn)