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 author = {Lamport, Leslie},
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 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}

@article{geethanath2013compressed,
  title={Compressed sensing MRI: a review},
  author={Geethanath, Sairam and Reddy, Rashmi and Konar, Amaresha Shridhar and Imam, Shaikh and Sundaresan, Rajagopalan and DR, Ramesh Babu and Venkatesan, Ramesh},
  journal={Critical Reviews in Biomedical Engineering},
  volume={41},
  number={3},
  year={2013},
  publisher={Begel House Inc.}
}

@article{yang2025improving,
  title={Improving the sparse coding model via hybrid Gaussian priors},
  author={Yang, Lijian and Mi, Jianxun and Li, Weisheng and Wang, Guofen and Xiao, Bin},
  journal={Pattern Recognition},
  volume={159},
  pages={111102},
  year={2025},
  publisher={Elsevier}
}


@article{chung2022score,
  title={Score-based diffusion models for accelerated MRI},
  author={Chung, Hyungjin and Ye, Jong Chul},
  journal={Medical image analysis},
  volume={80},
  pages={102479},
  year={2022},
  publisher={Elsevier}
}

@article{zbontar2018fastmri,
  title={fastMRI: An open dataset and benchmarks for accelerated MRI},
  author={Zbontar, Jure and Knoll, Florian and Sriram, Anuroop and Murrell, Tullie and Huang, Zhengnan and Muckley, Matthew J and Defazio, Aaron and Stern, Ruben and Johnson, Patricia and Bruno, Mary and others},
  journal={arXiv preprint arXiv:1811.08839},
  year={2018}
}


@article{ekanayake2025cl,
  title={CL-MRI: Self-Supervised contrastive learning to improve the accuracy of undersampled MRI reconstruction},
  author={Ekanayake, Mevan and Chen, Zhifeng and Harandi, Mehrtash and Egan, Gary and Chen, Zhaolin},
  journal={Biomedical Signal Processing and Control},
  volume={100},
  pages={107185},
  year={2025},
  publisher={Elsevier}
}

@article{lustig2007sparse,
  title={Sparse MRI: The application of compressed sensing for rapid MR imaging},
  author={Lustig, Michael and Donoho, David and Pauly, John M},
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  volume={58},
  number={6},
  pages={1182--1195},
  year={2007},
  publisher={Wiley Online Library}
}

@article{lustig2008compressed,
  title={Compressed sensing MRI},
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  journal={IEEE signal processing magazine},
  volume={25},
  number={2},
  pages={72--82},
  year={2008},
  publisher={IEEE}
}

@article{ravishankar2010mr,
  title={MR image reconstruction from highly undersampled k-space data by dictionary learning},
  author={Ravishankar, Saiprasad and Bresler, Yoram},
  journal={IEEE transactions on medical imaging},
  volume={30},
  number={5},
  pages={1028--1041},
  year={2010},
  publisher={IEEE}
}

@article{zha2018compressed,
  title={Compressed sensing image reconstruction via adaptive sparse nonlocal regularization},
  author={Zha, Zhiyuan and Liu, Xin and Zhang, Xinggan and Chen, Yang and Tang, Lan and Bai, Yechao and Wang, Qiong and Shang, Zhenhong},
  journal={The Visual Computer},
  volume={34},
  number={1},
  pages={117--137},
  year={2018},
  publisher={Springer}
}

@article{tariyal2016deep,
  title={Deep dictionary learning},
  author={Tariyal, Snigdha and Majumdar, Angshul and Singh, Richa and Vatsa, Mayank},
  journal={IEEE Access},
  volume={4},
  pages={10096--10109},
  year={2016},
  publisher={Ieee}
}

@article{bao2019convolutional,
  title={Convolutional sparse coding for compressed sensing CT reconstruction},
  author={Bao, Peng and Xia, Wenjun and Yang, Kang and Chen, Weiyan and Chen, Mianyi and Xi, Yan and Niu, Shanzhou and Zhou, Jiliu and Zhang, He and Sun, Huaiqiang and others},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={11},
  pages={2607--2619},
  year={2019},
  publisher={IEEE}
}

@article{lewis2018solving,
  title={Solving inverse problems in imaging via deep dictionary learning},
  author={Lewis, John and Singhal, Vanika and Majumdar, Angshul},
  journal={IEEE Access},
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  pages={37039--37049},
  year={2018},
  publisher={IEEE}
}

@misc{Yang_2017_ADMM-NetDeepLearningApproachCompressiveSensingMRI,
  title = {{{ADMM-Net}}: {{A Deep Learning Approach}} for {{Compressive Sensing MRI}}},
  shorttitle = {{{ADMM-Net}}},
  author = {Yang, Yan and Sun, Jian and Li, Huibin and Xu, Zongben},
  year = 2017,
  month = may,
  number = {arXiv:1705.06869},
  eprint = {1705.06869},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1705.06869},
  urldate = {2024-06-15},
  archiveprefix = {arXiv}
}

@article{Ahmad_2020_Plug-and-PlayMethodsMagneticResonanceImagingUsingDenoisersImageRecovery,
  title = {Plug-and-{{Play Methods}} for {{Magnetic Resonance Imaging}}: {{Using Denoisers}} for {{Image Recovery}}},
  shorttitle = {Plug-and-{{Play Methods}} for {{Magnetic Resonance Imaging}}},
  author = {Ahmad, Rizwan and Bouman, Charles A. and Buzzard, Gregery T. and Chan, Stanley and Liu, Sizhuo and Reehorst, Edward T. and Schniter, Philip},
  year = 2020,
  month = jan,
  journal = {IEEE Signal Processing Magazine},
  volume = {37},
  number = {1},
  pages = {105--116},
  issn = {1558-0792},
  doi = {10.1109/MSP.2019.2949470},
  urldate = {2024-08-07},
  annotation = {TLDR: This article describes the use of plug-and-play (PnP) algorithms for MRI image recovery and describes how the result of the PnP method can be interpreted as a solution to an equilibrium equation, allowing convergence analysis from this perspective.}
}

@misc{Schlemper_2017_DeepCascadeConvolutionalNeuralNetworksDynamicMRImageReconstruction,
  title = {A {{Deep Cascade}} of {{Convolutional Neural Networks}} for {{Dynamic MR Image Reconstruction}}},
  author = {Schlemper, Jo and Caballero, Jose and Hajnal, Joseph V. and Price, Anthony and Rueckert, Daniel},
  year = 2017,
  month = nov,
  number = {arXiv:1704.02422},
  eprint = {1704.02422},
  primaryclass = {cs},
  publisher = {arXiv},
  urldate = {2024-06-15},
  archiveprefix = {arXiv}
}

@inproceedings{Venkatakrishnan_2013_Plug-playpriorsmodelbasedreconstruction,
  title = {Plug-and-Play Priors for Model Based Reconstruction},
  booktitle = {2013 {{IEEE Global Conference}} on {{Signal}} and {{Information Processing}}},
  author = {Venkatakrishnan, Singanallur V. and Bouman, Charles A. and Wohlberg, Brendt},
  year = 2013,
  month = dec,
  pages = {945--948},
  publisher = {IEEE},
  address = {Austin, TX, USA},
  doi = {10.1109/GlobalSIP.2013.6737048},
  urldate = {2024-09-11},
  isbn = {978-1-4799-0248-4}
}

@inproceedings{Zheng_2021_Deepconvolutionaldictionarylearningimagedenoising,
  title = {Deep Convolutional Dictionary Learning for Image Denoising},
  booktitle = {2021 {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {Zheng, Hongyi and Yong, Hongwei and Zhang, Lei},
  year = 2021,
  month = jun,
  pages = {630--641},
  publisher = {IEEE},
  address = {Nashville, TN, USA},
  doi = {10.1109/CVPR46437.2021.00069},
  urldate = {2024-09-10},
  copyright = {https://doi.org/10.15223/policy-029},
  isbn = {978-1-6654-4509-2},
  annotation = {TLDR: A novel framework of deep convolutional dictionary learning (DCDicL), which follows the representation model of DicL strictly, learns the priors for both representation coefficients and the dictionaries, and can adaptively adjust the dictionary for each input image based on its content.}
}

@article{JianchaoYang_2012_Coupleddictionarytrainingimagesuper-resolution,
  title = {Coupled Dictionary Training for Image Super-Resolution},
  author = {{Jianchao Yang} and {Zhaowen Wang} and {Zhe Lin} and Cohen, S. and Huang, T.},
  year = 2012,
  month = aug,
  journal = {IEEE Transactions on Image Processing},
  volume = {21},
  number = {8},
  pages = {3467--3478},
  issn = {1057-7149, 1941-0042},
  doi = {10.1109/TIP.2012.2192127},
  urldate = {2024-09-11},
  annotation = {JCR分区: Q1\\
中科院分区升级版: 计算机科学1区\\
影响因子: 10.8\\
5年影响因子: 12.1\\
EI: 是\\
南农高质量: A\\
TLDR: This paper demonstrates that the coupled dictionary learning method can outperform the existing joint dictionary training method both quantitatively and qualitatively and speed up the algorithm approximately 10 times by learning a neural network model for fast sparse inference and selectively processing only those visually salient regions.}
}

@misc{Zhang_2018_ISTA-NetInterpretableOptimization-InspiredDeepNetworkImageCompressiveSensinga,
  title = {{{ISTA-Net}}: {{Interpretable Optimization-Inspired Deep Network}} for {{Image Compressive Sensing}}},
  shorttitle = {{{ISTA-Net}}},
  author = {Zhang, Jian and Ghanem, Bernard},
  year = 2018,
  month = jun,
  number = {arXiv:1706.07929},
  eprint = {1706.07929},
  primaryclass = {cs},
  publisher = {arXiv},
  urldate = {2024-08-06},
  archiveprefix = {arXiv}
}


@article{Deng_2020_DeepcoupledISTAnetworkmulti-modalimagesuper-resolution,
  title = {Deep Coupled {{ISTA}} Network for Multi-Modal Image Super-Resolution},
  author = {Deng, Xin and Dragotti, Pier Luigi},
  year = 2020,
  journal = {IEEE Transactions on Image Processing},
  volume = {29},
  pages = {1683--1698},
  issn = {1057-7149, 1941-0042},
  doi = {10.1109/TIP.2019.2944270},
  urldate = {2024-09-12},
  annotation = {198 citations (Semantic Scholar/DOI) [2025-10-28]\\
TLDR: A model-based approach to design a new deep network architecture for MISR, and introduces a novel joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency and proposes a layer-wise optimization algorithm (LOA) to initialize the parameters of the network before running back-propagation strategy.}
}


@misc{Sriram_2020_End-to-EndVariationalNetworksAcceleratedMRIReconstruction,
  title = {End-to-{{End Variational Networks}} for {{Accelerated MRI Reconstruction}}},
  author = {Sriram, Anuroop and Zbontar, Jure and Murrell, Tullie and Defazio, Aaron and Zitnick, C. Lawrence and Yakubova, Nafissa and Knoll, Florian and Johnson, Patricia},
  year = 2020,
  month = apr,
  number = {arXiv:2004.06688},
  eprint = {2004.06688},
  primaryclass = {cs, eess},
  publisher = {arXiv},
  urldate = {2024-07-28},
  archiveprefix = {arXiv}
}

@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}

@inproceedings{gregor2010learning,
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  author={Gregor, Karol and LeCun, Yann},
  booktitle={Proceedings of the 27th international conference on international conference on machine learning},
  pages={399--406},
  year={2010}
}

@article{ekanayake2024mcstra,
  title={McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction},
  author={Ekanayake, Mevan and Pawar, Kamlesh and Harandi, Mehrtash and Egan, Gary and Chen, Zhaolin},
  journal={Computers in Biology and Medicine},
  volume={168},
  pages={107775},
  year={2024},
  publisher={Elsevier}
}

@article{yu2017deep,
  title={Deep de-aliasing for fast compressive sensing MRI},
  author={Yu, Simiao and Dong, Hao and Yang, Guang and Slabaugh, Greg and Dragotti, Pier Luigi and Ye, Xujiong and Liu, Fangde and Arridge, Simon and Keegan, Jennifer and Firmin, David and others},
  journal={arXiv preprint arXiv:1705.07137},
  year={2017}
}

@article{quan2018compressed,
  title={Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss},
  author={Quan, Tran Minh and Nguyen-Duc, Thanh and Jeong, Won-Ki},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={6},
  pages={1488--1497},
  year={2018},
  publisher={IEEE}
}

@inproceedings{song2024solving,
  title={Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency},
  author={Song, Bowen and Kwon, Soo Min and Zhang, Zecheng and Hu, Xinyu and Qu, Qing and Shen, Liyue},
  booktitle={International Conference on Learning Representations},
  year={2024}
}

@inproceedings{luo20253denhancer,
  title={3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement},
  author={Luo, Yihang and Zhou, Shangchen and Lan, Yushi and Pan, Xingang and Loy, Chen Change},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={16430--16440},
  year={2025}
}

@inproceedings{chung2024decomposed,
  title={DECOMPOSED DIFFUSION SAMPLER FOR ACCELERATING LARGE-SCALE INVERSE PROBLEMS},
  author={Chung, Hyungjin and Lee, Suhyeon and Ye, Jong Chul},
  booktitle={International Conference on Learning Representations},
  year={2024}
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@inproceedings{jiang2024fast,
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  author={Jiang, Wei and Xiong, Zhuang and Liu, Feng and Ye, Nan and Sun, Hongfu},
  booktitle={2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  pages={1--5},
  year={2024},
  organization={IEEE}
}

@article{chung2022diffusion,
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  author={Chung, Hyungjin and Kim, Jeongsol and Mccann, Michael T and Klasky, Marc L and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2209.14687},
  year={2022}
}

@article{wang2022zero,
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  author={Wang, Yinhuai and Yu, Jiwen and Zhang, Jian},
  journal={arXiv preprint arXiv:2212.00490},
  year={2022}
}

@article{dhariwal2021diffusion,
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  author={Dhariwal, Prafulla and Nichol, Alexander},
  journal={Advances in neural information processing systems},
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  pages={8780--8794},
  year={2021}
}

@article{uecker2014espirit,
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  author={Uecker, Martin and Lai, Peng and Murphy, Mark J and Virtue, Patrick and Elad, Michael and Pauly, John M and Vasanawala, Shreyas S and Lustig, Michael},
  journal={Magnetic resonance in medicine},
  volume={71},
  number={3},
  pages={990--1001},
  year={2014},
  publisher={Wiley Online Library}
}

@article{song2020denoising,
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  author={Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
  journal={arXiv preprint arXiv:2010.02502},
  year={2020}
}

@inproceedings{esser2021taming,
  title={Taming transformers for high-resolution image synthesis},
  author={Esser, Patrick and Rombach, Robin and Ommer, Bjorn},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={12873--12883},
  year={2021}
}

@article{antun2020instabilities,
  title={On instabilities of deep learning in image reconstruction and the potential costs of AI},
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  publisher={National Academy of Sciences}
}

@article{yang2012coupled,
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  volume={21},
  number={8},
  pages={3467--3478},
  year={2012},
  publisher={IEEE}
}

@inproceedings{wang2024progressive,
  title={Progressive divide-and-conquer via subsampling decomposition for accelerated mri},
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  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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  year={2024}
}

@article{guan2023subspace,
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  year={2023},
  publisher={IEEE}
}

@inproceedings{jing2022subspace,
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  year={2022},
  organization={Springer}
}

@article{guan2024sub,
  title={Sub-DM: Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction},
  author={Guan, Yu and Cai, Qinrong and Li, Wei and Fan, Qiuyun and Liang, Dong and Liu, Qiegen},
  journal={arXiv preprint arXiv:2411.03758},
  year={2024}
}
