[18] T. Huang, S. Li, X. Jia, H. Lu, and J. Liu. "Neighbor2Neighbor: Self-Supervised Denoising From Single Noisy Images". In: IEEE Conference on Computer Vision and Pattern Recognition. 2021.
[19] Y. Huang, N. Zhang, and Q. Hao. "Real-Time Noise Reduction Based on Ground Truth Free Deep Learning for Optical Coherence Tomography". In: Biomedical Optics Express (2021).
[20] J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. "Scaling Laws for Neural Language Models". In: arXiv:2001.08361 [cs, stat] (2020).
[21] D. P. Kingma and J. Ba. "Adam: A Method for Stochastic Optimization". In: International Conference on Learning Representations (2014).
[22] T. Klug and R. Heckel. "Scaling Laws For Deep Learning Based Image Reconstruction". In: International Conference on Learning Representations. 2023.
[23] S. Lapins, A. Butcher, J.-M. Kendall, T. S. Hudson, A. L. Stork, M. J. Werner, J. Gunning, and A. M. Brisbane. "DAS-N2N: Machine Learning Distributed Acoustic Sensing (DAS) Signal Denoising without Clean Data". In: arXiv:2304.08120 [physics] (2023).
[24] J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila. "Noise2Noise: Learning Image Restoration without Clean Data". In: International Conference on Machine Learning. 2018.
[25] J. Lequyer, R. Philip, A. Sharma, W.-H. Hsu, and L. Pelletier. "A Fast Blind Zero-Shot Denoiser". In: Nature Machine Intelligence (2022).
[26] J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte. "SwinIR: Image Restoration Using Swin Transformer". In: IEEE International Conference on Computer Vision Workshops. 2021.
[27] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo. "Swin Transformer: Hierarchical Vision Transformer Using Shfted Windows". In: IEEE International Conference on Computer Vision. 2021.
[28] Y. Mansour and R. Heckel. "Zero-Shot Noise2Noise: Efficient Image Denoising Without Any Data". In: IEEE Conference on Computer Vision and Pattern Recognition. 2023.
[29] C. Millard and M. Chiew. “A Theoretical Framework for Self-Supervised MR Image Reconstruction Using Sub-Sampling via Variable Density Noisier2Noise”. In: arXiv:2205.10278 [cs, eess] (2023).
[30] N. Moran, D. Schmidt, Y. Zhong, and P. Coady. "Noisier2Noise: Learning to Denoise From Unpaired Noisy Data". In: IEEE Conference on Computer Vision and Pattern Recognition. 2020.
[31] M. J. Muckley et al. "Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction". In: IEEE Transactions on Medical Imaging (2021).
[32] A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro. "Robust Stochastic Approximation Approach to Stochastic Programming". In: SIAM Journal on Optimization (2009).
[33] T. Plotz and S. Roth. "Benchmarking Denoising Algorithms With Real Photographs". In: IEEE Conference on Computer Vision and Pattern Recognition. 2017.
[34] K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger. "SENSE: Sensitivity Encoding for Fast MRI". In: Magnetic Resonance in Medicine (1999).
[35] A. Rakhlin, O. Shamir, and K. Sridharan. "Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization". In: International Conference on Machine Learning. 2012.
[36] O. Ronneberger, P. Fischer, and T. Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation". In: Medical Image Computing and Computer-Assisted Intervention (2015).
[37] O. Russakovsky et al. "ImageNet Large Scale Visual Recognition Challenge". In: International Journal of Computer Vision (2015).
[38] A. Sriram, J. Zbontar, T. Murrell, A. Defazio, C. L. Zitnick, N. Yakubova, F. Knoll, and P. Johnson. "End-to-End Variational Networks for Accelerated MRI Reconstruction". In: Medical Image Computing and Computer Assisted Intervention. 2020.
[39] M. Uecker, P. Lai, M. J. Murphy, P. Virtue, M. Elad, J. M. Pauly, S. S. Vasanawala, and M. Lustig. "ESPIRiT - an Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE Meets GRAPPA". In: Magnetic Resonance in Medicine (2014).