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Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising.
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI (2015)
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Weigert, M., Royer, L., Jug, F., Myers, G.: Isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI (2017)
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Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing
2017
Cited alongside, same era.
Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., Aila, T.: Noise2Noise: Learning image restoration without clean data. In: ICML. pp. 2965–2974 (2018)
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Cited alongside, same era.
Weigert, M., , et al.: Content-aware image restoration: Pushing the limits of fluorescence microscopy. Nature Methods (2018). https://doi.org/10.1038/s41592-018-0216-7,
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Closest in time.
Buchholz, T.O., Jordan, M., Pigino, G., Jug, F.: Cryo-care: Content-aware image restoration for cryo-transmission electron microscopy data. In: ISBI (2019)
2019
Closest in time.
Krull, A., Buchholz, T.O., Jug, F.: Noise2void - learning denoising from single noisy images. In: CVPR (2019)
2019
Closest in time.
2019
Closest in time.
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