[1] A. Abdelhamed, S. Lin, and M. S. Brown. "A High-Quality Denoising Dataset for Smartphone Cameras". In: IEEE Conference on Computer Vision and Pattern Recognition. 2018.
[2] J. Batson and L. Royer. "Noise2Self: Blind Denoising by Self-Supervision". In: International Conference on Machine Learning. 2019.
[3] T. Bepler, K. Kelley, A. J. Noble, and B. Berger. "Topaz-Denoise: General Deep Denoising Models for cryoEM and cryoET". In: Nature Communications (2020).
[4] T. Brooks, B. Mildenhall, T. Xue, J. Chen, D. Sharlet, and J. T. Barron. "Unprocessing Images for Learned Raw Denoising". In: IEEE Conference on Computer Vision and Pattern Recognition. 2019.
[5] T.-O. Buchholz, M. Jordan, G. Pigino, and F. Jug. "Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data". In: IEEE International Symposium on Biomedical Imaging. 2019.
[6] M. Z. Darestani and R. Heckel. "Accelerated MRI With Un-Trained Neural Networks". In: IEEE Transactions on Computational Imaging (2021).
[7] A. D. Desai, B. Gunel, B. M. Ozturkler, H. Beg, S. Vasanawala, B. A. Hargreaves, C. Ré, J. M. Pauly, and A. S. Chaudhari. "VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction". In: Medical Imaging with Deep Learning. 2022.
[8] C. Dong, C. C. Loy, K. He, and X. Tang. "Image Super-Resolution Using Deep Convolutional Networks". In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2016).
[9] Y. Dubois, T. Hashimoto, and P. Liang. "Evaluating Self-Supervised Learning via Risk Decomposition". In: arXiv:2302.03068 [cs, stat] (2023).
[10] Z. Fabian, R. Heckel, and M. Soltanolkotabi. "Data Augmentation for Deep Learning Based Accelerated MRI Reconstruction with Limited Data". In: International Conference on Machine Learning. 2021.
[11] Z. Fabian, B. Tinaz, and M. Soltanolkotabi. “HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction”. In: Advances in Neural Information Processing Systems (2022).
[12] J. Gurrola-Ramos, O. Dalmau, and T. E. Alarcón. "A Residual Dense U-Net Neural Network for Image Denoising". In: IEEE Access (2021).
[13] K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll. "Learning a Variational Network for Reconstruction of Accelerated MRI Data". In: Magnetic Resonance in Medicine (2018).
[14] R. Heckel and P. Hand. "Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks". In: International Conference on Learning Representations (2019).
[15] R. Heckel and M. Soltanolkotabi. "Compressive Sensing with Un-Trained Neural Networks: Gradient Descent Finds the Smoothest Approximation". In: International Conference on Machine Learning (2020).
[16] C. Hu, C. Li, H. Wang, Q. Liu, H. Zheng, and S. Wang. "Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework". In: Medical Image Computing and Computer Assisted Intervention. 2021.
[17] P. Huang, C. Zhang, H. Li, S. K. Gaire, R. Liu, X. Zhang, X. Li, L. Dong, and L. Ying. "Unsupervised Deep Learning Reconstruction Using the MR Imaging Model". In: Annual Meeting of the International Society for Magnetic Resonance in Medicine 28 (2020).