Daniël Pelt

Full Name

D.M. Pelt

Email

Daniel.Pelt@cwi.nl

Department(s)

Computational Imaging

Function(s)

Researcher

Daniël Pelt

Schoonhoven, R., Skorikov, A., Palenstijn, W. J., Pelt, D., Hendriksen, A., & Batenburg, J. (2024). How auto-differentiation can improve CT workflows: Classical algorithms in a modern framework. Optics Express, 32(6), 9019–9041. doi:10.1364/OE.502920
Shi, J., Pelt, D., & Batenburg, J. (2023). SR4ZCT: Self-supervised through-plane resolution enhancement for CT images with arbitrary resolution and overlap. In Machine Learning in Medical Imaging (pp. 52–61). doi:10.1007/978-3-031-45673-2_6
Vădineanu, Ş., Pelt, D., Dzyubachyk, O., & Batenburg, J. (2023). Reducing manual annotation costs for cell segmentation by upgrading low-quality annotations. In Workshop on Medical Image Learning with Limited and Noisy Data (pp. 3–13). doi:10.1007/978-3-031-44917-8_1
Zeegers, M., van Leeuwen, T., Pelt, D., Coban, S., van Liere, R., & Batenburg, J. (2022). A tomographic workflow to enable deep learning for X-ray based foreign object detection. Expert Systems with Applications, 206, 117768:1–117768:15. doi:10.1016/j.eswa.2022.117768
Kim, J., Pelt, D., Kagias, M., Stampanoni, M., Batenburg, J., & Marone, F. (2022). Tomographic reconstruction of the small-angle X-ray scattering tensor with filtered back projection. Physical Review Applied, 18(1). doi:10.1103/PhysRevApplied.18.014043
Pelt, D., Hendriksen, A., & Batenburg, J. (2022). Foam-like phantoms for comparing tomography algorithms. Journal of Synchrotron Radiation, 29, 254–265. doi:10.1107/S1600577521011322
Hendriksen, A., Schut, D., Palenstijn, W. J., Viganò, N., Kim, J., Pelt, D., … Batenburg, J. (2021). Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python. Optics Express, 29(24), 40494–40513. doi:10.1364/OE.439909
Ganguly, P., Pelt, D., Gürsoy, D., De Carlo, F., & Batenburg, J. (2021). Improving reproducibility in synchrotron tomography using implementation-adapted filters. Journal of Synchrotron Radiation, 28, 1583–1597. doi:10.1107/S1600577521007153
Hendriksen, A., Bührer, M., Leone, L., Merlini, M., Viganò, N., Pelt, D., … Batenburg, J. (2021). Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data. Nature Scientific Reports, 11. doi:10.1038/s41598-021-91084-8
Zeegers, M., Pelt, D., van Leeuwen, T., van Liere, R., & Batenburg, J. (2020). Task-driven learned hyperspectral data reduction using end-to-end supervised deep learning. Journal of Imaging, 6(12). doi:10.3390/jimaging6120132

Learning algorithms for large 3D images (2018)