Abstract
Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. While achieving remarkable results, self-supervised methods can produce high-frequency artifacts and achieve inferior results compared to supervised approaches. Here we present a novel way to improve the quality of self-supervised denoising. Considering that light microscopy images are usually diffraction-limited, we propose to include this knowledge in the denoising process. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network. As a consequence, we are able to eliminate high-frequency artifacts and achieve self-supervised results that are very close to the ones achieved with traditional supervised methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Code Availability.
Our code is available at https://github.com/juglab/DecoNoising.
References
Batson, J., Royer, L.: Noise2Self: blind denoising by self-supervision (2019)
Belthangady, C., Royer, L.A.: Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16, 1–11 (2019)
Buchholz, T.O., Prakash, M., Krull, A., Jug, F.: DenoiSeg: joint denoising and segmentation. arXiv preprint arXiv:2005.02987 (2020)
Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data. IEEE Trans. Image Process. 17(10), 1737–1754 (2008)
Hendriksen, A.A., Pelt, D.M., Batenburg, K.J.: Noise2Inverse: self-supervised deep convolutional denoising for linear inverse problems in imaging (2020)
Khademi, W., Rao, S., Minnerath, C., Hagen, G., Ventura, J.: Self-supervised Poisson-Gaussian denoising. arXiv preprint arXiv:2002.09558 (2020)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, 14–16 April 2014, Conference Track Proceedings (2014). http://arxiv.org/abs/1312.6114
Kobayashi, H., Solak, A.C., Batson, J., Royer, L.A.: Image deconvolution via noise-tolerant self-supervised inversion. arXiv preprint arXiv:2006.06156 (2020)
Krull, A., Buchholz, T.O., Jug, F.: Noise2Void-learning denoising from single noisy images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2129–2137 (2019)
Krull, A., Vicar, T., Prakash, M., Lalit, M., Jug, F.: Probabilistic Noise2Void: unsupervised content-aware denoising. Front. Comput. Sci. 2, 60 (2020)
Laine, S., Karras, T., Lehtinen, J., Aila, T.: High-quality self-supervised deep image denoising. In: Advances in Neural Information Processing Systems, pp. 6968–6978 (2019)
Lehtinen, J., et al.: Noise2noise: learning image restoration without clean data. In: International Conference on Machine Learning, pp. 2965–2974 (2018)
Luisier, F., Blu, T., Unser, M.: Image denoising in mixed Poisson-Gaussian noise. IEEE Trans. Image Process. 20(3), 696–708 (2010)
Marsh, R.: The Beetle. Broadview Press (2004)
Metzler, C.A., Mousavi, A., Heckel, R., Baraniuk, R.G.: Unsupervised learning with stein’s unbiased risk estimator. arXiv preprint arXiv:1805.10531 (2018)
Prakash, M., Krull, A., Jug, F.: Divnoising: diversity denoising with fully convolutional variational autoencoders. arXiv preprint arXiv:2006.06072 (2020)
Prakash, M., Lalit, M., Tomancak, P., Krull, A., Jug, F.: Fully unsupervised probabilistic Noise2Void. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 154–158. Iowa City, IA, USA (2020). https://doi.org/10.1109/ISBI45749.2020.9098612
Ramani, S., Blu, T., Unser, M.: Monte-Carlo sure: a black-box optimization of regularization parameters for general denoising algorithms. IEEE Trans. Image Process. 17(9), 1540–1554 (2008)
Raphan, M., Simoncelli, E.P.: Learning to be Bayesian without supervision. In: Advances in Neural Information Processing Systems, pp. 1145–1152 (2007)
Richardson, W.H.: Bayesian-based iterative method of image restoration. JoSA 62(1), 55–59 (1972)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Stein, C.M.: Estimation of the mean of a multivariate normal distribution. Ann. Stat. 9(6), 1135–1151 (1981)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: CVPR (2018)
Weigert, M., et al.: Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15(12), 1090–1097 (2018)
Zhang, Y., et al.: A Poisson-Gaussian denoising dataset with real fluorescence microscopy images. In: CVPR (2019)
Zhou, R., Helou, M.E., Sage, D., Laroche, T., Seitz, A., Süsstrunk, S.: W2S: a joint denoising and super-resolution dataset. arXiv preprint arXiv:2003.05961 (2020)
Acknowledgments
We thank the Scientific Computing Facility at MPI-CBG for giving us access to their HPC cluster.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Goncharova, A.S., Honigmann, A., Jug, F., Krull, A. (2020). Improving Blind Spot Denoising for Microscopy. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12535. Springer, Cham. https://doi.org/10.1007/978-3-030-66415-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-66415-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66414-5
Online ISBN: 978-3-030-66415-2
eBook Packages: Computer ScienceComputer Science (R0)