[1] LUSTIG M,DONOHO D L,SANTOS J M,et al. Compressed sensing MRI[J]. IEEE Signal Processing Magazine,2008,25(2):72-82. [2] 刘芳, 吴娇, 杨淑媛, 等. 结构化压缩感知进展[J]. 自动化学报, 2013,39(12):1980-1995.(LIU F,WU J,YANG S Y,et al. Research advances on structured compressive sensing[J]. Acta Automatica Sinica,2013,39(12):1980-1995.) [3] HUANG J, ZHANG S, METAXAS D. Efficient MR image reconstruction for compressed MR imaging[J]. Medical Image Analysis,2011,15(5):670-679. [4] CHEN C,HUANG J. Compressive sensing MRI with wavelet tree sparsity[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2012:1115-1123. [5] RAVISHANKAR S,BRESLER Y. MR image reconstruction from highly undersampled k-space data by dictionary learning[J]. IEEE Transactions on Medical Imaging,2011,30(5):1028-1041. [6] EKSIOGLU E M. Decoupled algorithm for MRI reconstruction using nonlocal block matching model:BM3D-MRI[J]. Journal of Mathematical Imaging and Vision,2016,56(3):430-440. [7] SHI B, LIAN Q, CHEN S. Compressed sensing magnetic resonance imaging based on dictionary updating and block-matching and three-dimensional filtering regularisation[J]. IET Image Processing,2016,10(1):68-79. [8] EKSIOGLU E M, TANC A K. Denoising AMP for MRI reconstruction:BM3D-AMP-MRI[J]. SIAM Journal on Imaging Sciences,2018,11(3):2090-2109. [9] YANG Y,SUN J,LI H,et al. ADMM-CSNet:a deep learning approach for image compressive sensing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(3):521-538. [10] JIN K H, MCCANN M T, FROUSTEY R, et al. Deep convolutional neural network for inverse problems in imaging[J]. IEEE Transactions on Image Processing,2017,26(9):4509-4522. [11] HAMMERNIK K,KLATZER T,KOBLER E,et al. Learning a variational network for reconstruction of accelerated MRI data[J]. Magnetic Resonance in Medicine,2018,79(6):3055-3071. [12] YANG G,YU S,DONG H,et. al. DAGAN:deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction[J]. IEEE Transactions on Medical Imaging,2018, 37(6):1310-1321. [13] DABOV K,FOI A,KATKOVNIK V,et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing,2007,16(8):2080-2095. [14] SHI B, LIAN Q, CHANG H. Deep prior-based sparse representation model for diffraction imaging:a plug-and-play method[J]. Signal Processing,2020,168:No. 107350. [15] 练秋生, 石保顺, 陈书贞. 字典学习模型、算法及其应用研究进展[J]. 自动化学报,2015,41(2):240-260.(LIAN Q S,SHI B S,CHEN S Z. Research advances on dictionary learning models, algorithms and applications[J]. Acta Automatica Sinica,2015,41(2):240-260.) [16] ZHANG L,ZHANG L,MOU X,et al. FSIM:a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing,2011,20(8):2378-2386. [17] ZHANG K,ZUO W,ZHANG L. FFDNet:toward a fast and flexible solution for CNN based image denoising[J]. IEEE Transactions on Image Processing,2018,27(9):4608-4622. |