# Image denoising for CT scans

## VG Sample Data

[download](https://hexmet-my.sharepoint.com/personal/patrick_fuchs_hexagon_com/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fpatrick%5Ffuchs%5Fhexagon%5Fcom%2FDocuments%2FProjects%2FITT%2Fdata&ga=1)

## Literature

### Tutorials / general introductions
* J. Rafid Siddiqui [Diffusion Models Made Easy](https://towardsdatascience.com/diffusion-models-made-easy-8414298ce4da)

### Review articles
* Buades, A., Coll, B. and Morel, J.M., 2005. *A review of image denoising algorithms, with a new one.* Multiscale modeling & simulation, 4(2), pp.490-530. [link](https://epubs.siam.org/doi/pdf/10.1137/040616024)
* Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W. and Lin, C.W., 2020. *Deep learning on image denoising: An overview.* Neural Networks, 131, pp.251-275. [link](https://www.sciencedirect.com/science/article/pii/S0893608020302665)

### Application related
* P. Fuchs, T Kröger, C. S. Garbe, 2021. *Defect detection in CT scans of cast aluminum parts: A machine vision perspective* Neurocomputing, 453, pp.85-96. [paper](https://www.sciencedirect.com/science/article/pii/S0925231221006524/pdfft?md5=f03ffcea558f1264bd2bcfe1f2d88eec&pid=1-s2.0-S0925231221006524-main.pdf)
* Heylen, Rob, et al. "3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts." Measurement Science and Technology 33.4 (2022): 045602. [paper](https://iopscience.iop.org/article/10.1088/1361-6501/ac459a/pdf)
* Bellens, Simon, Patrick Vandewalle, and Wim Dewulf. "Deep learning based porosity segmentation in X-ray CT measurements of polymer additive manufacturing parts." Procedia CIRP 96 (2021): 336-341. [paper](https://www.sciencedirect.com/science/article/pii/S2212827121001888/pdf?md5=6dd3287fbcd3b07d7352495a6d2278f3&pid=1-s2.0-S2212827121001888-main.pdf)
* Rodríguez-Sánchez, Ángela, et al. "Review of the influence of noise in X-ray computed tomography measurement uncertainty." Precision Engineering 66 (2020): 382-391. [paper](https://www.sciencedirect.com/science/article/pii/S0141635920306012/pdfft?md5=fcf12f735e2ab5eef4c997cdc130b387&pid=1-s2.0-S0141635920306012-main.pdf)
### Classical approaches

### Machine learning
* Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. and Ganguli, S., 2015, June. *Deep unsupervised learning using nonequilibrium thermodynamics.* In International Conference on Machine Learning (pp. 2256-2265). PMLR. [arXiv:1503.03585](https://arxiv.org/abs/1503.03585)
* Ho, J., Jain, A. and Abbeel, P., 2020. *Denoising diffusion probabilistic models.* Advances in Neural Information Processing Systems, 33, pp.6840-6851. [arXiv:2006.11239](https://arxiv.org/abs/2006.11239)
* Paavani Dua: [Image Denoising Using a U-net](http://stanford.edu/class/ee367/Winter2019/dua_report.pdf)
* Fan, C.M., Liu, T.J. and Liu, K.H., 2022. *SUNet: Swin Transformer UNet for Image Denoising.* preprint [arXiv:2202.14009](https://arxiv.org/abs/2202.14009).