CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization
暂无分享,去创建一个
Hongwei Lu | Hongqing Liu | Jianxin Cao | Shujun Liu | Hongqing Liu | Shujun Liu | Jianxin Cao | Hongwei Lu
[1] J. Dodziuk. Difference equations, isoperimetric inequality and transience of certain random walks , 1984 .
[2] Dong Liang,et al. Highly Undersampled Magnetic Resonance Image Reconstruction Using Two-Level Bregman Method With Dictionary Updating , 2013, IEEE Transactions on Medical Imaging.
[3] Lei Zhang,et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.
[4] Zhigang Luo,et al. Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent , 2011, IEEE Transactions on Image Processing.
[5] Yujie Li,et al. Manifold optimization-based analysis dictionary learning with an ℓ1∕2-norm regularizer , 2018, Neural Networks.
[6] Jun Yu,et al. Click Prediction for Web Image Reranking Using Multimodal Sparse Coding , 2014, IEEE Transactions on Image Processing.
[7] Leslie Ying,et al. Accelerating magnetic resonance imaging via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[8] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.
[9] Jian-Feng Cai,et al. Split Bregman Methods and Frame Based Image Restoration , 2009, Multiscale Model. Simul..
[10] Michael Elad,et al. Analysis versus synthesis in signal priors , 2006, 2006 14th European Signal Processing Conference.
[11] Yoram Bresler,et al. MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning , 2011, IEEE Transactions on Medical Imaging.
[12] Yunmei Chen,et al. A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data , 2010 .
[13] Yoram Bresler,et al. Learning overcomplete sparsifying transforms for signal processing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[14] Rémi Gribonval,et al. Constrained Overcomplete Analysis Operator Learning for Cosparse Signal Modelling , 2012, IEEE Transactions on Signal Processing.
[15] Wen Gao,et al. Group-Based Sparse Representation for Image Restoration , 2014, IEEE Transactions on Image Processing.
[16] Alessandro Foi,et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.
[17] Pietro Lio',et al. How Can We Make Gan Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[18] Jian Sun,et al. Deep ADMM-Net for Compressive Sensing MRI , 2016, NIPS.
[19] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[20] Christopher G. Roth,et al. Optimizing abdominal MR imaging: approaches to common problems. , 2010, Radiographics : a review publication of the Radiological Society of North America, Inc.
[21] Kyoung Mu Lee,et al. Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Yonina C. Eldar,et al. Compressed Sensing with Coherent and Redundant Dictionaries , 2010, ArXiv.
[23] Dong Liang,et al. Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery , 2013, IEEE Transactions on Image Processing.
[24] Mário A. T. Figueiredo,et al. Signal restoration with overcomplete wavelet transforms: comparison of analysis and synthesis priors , 2009, Optical Engineering + Applications.
[25] Di Guo,et al. Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction , 2015, IEEE Transactions on Biomedical Engineering.
[26] Gary H Glover,et al. Increasing spatial coverage for high‐resolution functional MRI , 2009, Magnetic resonance in medicine.
[27] Guang Yang,et al. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.
[28] Di Guo,et al. Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator , 2014, Medical Image Anal..
[29] Guangming Shi,et al. Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach , 2013, IEEE Transactions on Image Processing.
[30] Guang Yang,et al. Lesion Focused Super-Resolution , 2018, Medical Imaging: Image Processing.
[31] Zhong Chen,et al. Undersampled MRI reconstruction with patch-based directional wavelets. , 2012, Magnetic resonance imaging.
[32] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[33] Xueqi Ma,et al. $p$ -Laplacian Regularization for Scene Recognition , 2019, IEEE Transactions on Cybernetics.
[34] Stephen P. Boyd,et al. Proximal Algorithms , 2013, Found. Trends Optim..
[35] Shiqian Ma,et al. An efficient algorithm for compressed MR imaging using total variation and wavelets , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[36] Michael Elad,et al. K-SVD dictionary-learning for the analysis sparse model , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[37] J. Borwein,et al. Two-Point Step Size Gradient Methods , 1988 .
[38] Leo Grady,et al. Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Di Guo,et al. Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization. , 2013, Magnetic resonance imaging.
[40] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[41] Ke Lu,et al. $p$-Laplacian Regularized Sparse Coding for Human Activity Recognition , 2016, IEEE Transactions on Industrial Electronics.
[42] Shujun Liu,et al. Group sparsity with orthogonal dictionary and nonconvex regularization for exact MRI reconstruction , 2018, Inf. Sci..
[43] Daniel Rueckert,et al. A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction , 2017, IPMI.
[44] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[45] Junfeng Yang,et al. A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data , 2010, IEEE Journal of Selected Topics in Signal Processing.
[46] Ender M. Eksioglu,et al. K-SVD Meets Transform Learning: Transform K-SVD , 2014, IEEE Signal Processing Letters.
[47] Mathews Jacob,et al. Iterative Shrinkage Algorithm for Patch-Smoothness Regularized Medical Image Recovery , 2015, IEEE Transactions on Medical Imaging.
[48] Guang Yang,et al. Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction , 2018, MICCAI.
[49] I. Daubechies,et al. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.
[50] Michael Elad,et al. Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model , 2013, IEEE Transactions on Signal Processing.
[51] Rabab Kreidieh Ward,et al. On the choice of Compressed Sensing priors and sparsifying transforms for MR image reconstruction: An experimental study , 2012, Signal Process. Image Commun..
[52] Guangming Shi,et al. Denoising Prior Driven Deep Neural Network for Image Restoration , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] Guang Yang,et al. Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI , 2018, MICCAI.
[54] Di Guo,et al. Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging , 2015, IEEE Transactions on Medical Imaging.
[55] Zhong Chen,et al. Balanced Sparse Model for Tight Frames in Compressed Sensing Magnetic Resonance Imaging , 2015, PloS one.
[56] Lei Zhang,et al. Nonlocally Centralized Sparse Representation for Image Restoration , 2013, IEEE Transactions on Image Processing.
[57] Junzhou Huang,et al. Efficient MR image reconstruction for compressed MR imaging , 2011, Medical Image Anal..
[58] Wotao Yin,et al. A feasible method for optimization with orthogonality constraints , 2013, Math. Program..
[59] Yoram Bresler,et al. $\ell_{0}$ Sparsifying Transform Learning With Efficient Optimal Updates and Convergence Guarantees , 2015, IEEE Transactions on Signal Processing.
[60] M. Elad,et al. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.
[61] Yong Luo,et al. Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification , 2013, IEEE Transactions on Image Processing.
[62] D. Donoho,et al. Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.
[63] K. T. Block,et al. Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint , 2007, Magnetic resonance in medicine.
[64] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[65] Yi Zhou,et al. Simultaneous Radio Frequency and Wideband Interference Suppression in SAR Signals via Sparsity Exploitation in Time–Frequency Domain , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[66] Guangming Shi,et al. Compressive Sensing via Nonlocal Low-Rank Regularization , 2014, IEEE Transactions on Image Processing.
[67] Qianjin Feng,et al. Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data , 2015, Physics in medicine and biology.