Towards Structured Noise Models for Unsupervised Denoising-Reference-Cited by-同舟云学术

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Towards Structured Noise Models for Unsupervised Denoising

Published:2023 Issue: Volume: Page:379-394
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Salmon Benjamin^ORCID,Krull Alexander^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-25069-9_25

Reference30 articles.

1. Abdelhamed, A., Brubaker, M.A., Brown, M.S.: Noise flow: noise modeling with conditional normalizing flows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3165–3173 (2019)

2. Babcock, H.P., Huang, F., Speer, C.M.: Correcting artifacts in single molecule localization microscopy analysis arising from pixel quantum efficiency differences in sCMOS cameras. Sci. Rep. 9(1), 1–10 (2019)

3. Batson, J., Royer, L.: Noise2self: blind denoising by self-supervision (2019)

4. Belthangady, C., Royer, L.A.: Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16(12), 1215–1225 (2019)

5. Bond-Taylor, S., Leach, A., Long, Y., Willcocks, C.G.: Deep generative modelling: a comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models. arXiv preprint arXiv:2103.04922 (2021)

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1. Advanced optical imaging for the rational design of nanomedicines;Advanced Drug Delivery Reviews;2024-01

2. Direct Unsupervised Denoising;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

3. Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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