Optics and Precision Engineering, Volume. 32, Issue 14, 2311(2024)
Iterative reconstruction of compressive sensing combining image hierarchical-feature
[1] D L DONOHO. Compressed sensing. IEEE Transactions on Information Theory, 52, 1289-1306(2006).
[2] M F DUARTE, M A DAVENPORT, D TAKHAR et al. Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25, 83-91(2008).
[3] L ZHU, X WU, Z Y SUN et al. Compressed-sensing accelerated 3-dimensional magnetic resonance cholangiopancreatography: application in suspected pancreatic diseases. Investigative Radiology, 53, 150-157(2018).
[4] X YUAN, D J BRADY, A K KATSAGGELOS. Snapshot compressive imaging: theory, algorithms, and applications. IEEE Signal Processing Magazine, 38, 65-88(2021).
[5] S KUMAR, M MAHADEVAPPA, P K DUTTA. Compressive holography from poisson noise plagued holograms using expectation-maximization. IEEE Transactions on Computational Imaging, 6, 857-867(2020).
[6] M TROCAN, E W TRAMEL, J E FOWLER et al. Compressed-sensing recovery of multiview image and video sequences using signal prediction. Multimedia Tools and Applications, 72, 95-121(2014).
[7] M V AFONSO, J M BIOUCAS-DIAS, M A T FIGUEIREDO. An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Transactions on Image Processing, 20, 681-695(2011).
[8] C A METZLER, A MALEKI, R G BARANIUK. From denoising to compressed sensing. IEEE Transactions on Information Theory, 62, 5117-5144(2016).
[9] İ BAYRAM. On the convergence of the iterative shrinkage/thresholding algorithm with a weakly convex penalty. IEEE Transactions on Signal Processing, 64, 1597-1608(2016).
[10] K KULKARNI, S LOHIT, P TURAGA et al. ReconNet: non-iterative reconstruction of images from compressively sensed measurements, 449-458(2016).
[11] W Z SHI, F JIANG, S H LIU et al. Image compressed sensing using convolutional neural network. 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] Z H ZHANG, Y P LIU, J N LIU et al. AMP-net: denoising-based deep unfolding for compressive image sensing. IEEE Transactions on Image Processing, 30, 1487-1500(2021).
[14] J ZHANG, B GHANEM. ISTA-net: interpretable optimization-inspired deep network for image compressive sensing, 1828-1837(2018).
[15] D YOU, J F XIE, J ZHANG. ISTA-NET: flexible deep unfolding network for compressive sensing, 1-6(2021).
[16] J ZHANG, C ZHAO, W GAO. Optimization-inspired compact deep compressive sensing. 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] X L WANG, R GIRSHICK, A GUPTA et al. Non-local neural networks, 7794-7803(2018).
[21] Z LIU, Y T LIN, Y CAO et al. Swin Transformer: hierarchical Vision Transformer using Shifted Windows, 9992-10002(2021).
[22] M H SHEN, H P GAN, C NING et al. TransCS: a transformer-based hybrid architecture for image compressed sensing. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 31, 6991-7005(2022).
[23] D J YE, Z K NI, H L WANG et al. CSformer: bridging convolution and transformer for compressive sensing. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 32, 2827-2842(2023).
[24] Y W LI, Y C FAN, X Y XIANG et al. Efficient and explicit modelling of image hierarchies for image restoration, 18278-18289(2023).
[25] M H GUO, T X XU, J J LIU et al. Attention mechanisms in computer vision: a survey. Computational Visual Media, 8, 331-368(2022).
[26] P ARBELÁEZ, M MAIRE, C FOWLKES et al. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 898-916(2011).
[27] J ZHANG, D B ZHAO, W GAO. Group-based sparse representation for image restoration. IEEE Transactions on Image Processing, 23, 3336-3351(2014).
[28] D MARTIN, C FOWLKES et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, 416-423(2001).
[29] J B HUANG, A SINGH, N AHUJA. Single image super-resolution from transformed self-exemplars, 5197-5206(2015).
[30] D P KINGMA, J BA. Adam: a method for stochastic optimization. ArXiv e-Prints(2014).
[31] I LOSHCHILOV, F HUTTER. SGDR: stochastic gradient descent with warm restarts. ArXiv e-Prints(2016).
[32] Z WANG, A C BOVIK, H R SHEIKH et al. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 13, 600-612(2004).
Get Citation
Copy Citation Text
Yuhong LIU, Heng YANG. Iterative reconstruction of compressive sensing combining image hierarchical-feature[J]. Optics and Precision Engineering, 2024, 32(14): 2311
Category:
Received: Mar. 1, 2024
Accepted: --
Published Online: Sep. 27, 2024
The Author Email: YANG Heng (11220697@stu.lzjtu.edu.cn)