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D
J. Driesen and Scheunders, P., “Wavelet based segmentation of multivalued images”, in SPIE Optics East, 23-26 October, Boston, Massachusetts USA, 2005, vol. 6001, pp. 13-22.
J. Driesen and Scheunders, P., “Wavelet based Filter Array Demosaicking”, in Proc. ICIP2004, IEEE International Conference on Image Processing, 24-27 october, Singapore, 2004, pp. 3311-3314.
J. Driesen and Scheunders, P., “Wavelet based segmentation of multi-component images”, in IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS) March 28-29, Antwerp, Belgium, 2006, pp. 151-154.
A. Duijster, Scheunders, P., and De Backer, S., “Wavelet-Based EM Algorithm for Multispectral-Image Restoration”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3892-3898, 2009.
A. Duijster, De Backer, S., and Scheunders, P., “Wavelet-based Multispectral Image Restoration”, in IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008, vol. 3, pp. 79-82.
A. Duijster, De Backer, S., and Scheunders, P., “Wavelet-based Multicomponent Image Restoration”, in Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, United States, September 9-12, 2007, vol. 6763.
A. Duijster, De Backer, S., and Scheunders, P., “Multicomponent image restoration, an experimental study”, Lecture Notes in Computer Science, vol. 4633, pp. 58-68, 2007.
K. Duquesne, Van Oevelen, A., Sijbers, J., Van Paepegem, W., and Audenaert, E., “A novel Soft Tissue-Integrated Kinematic Solver for skeletal motion: Validation and applications”, Computer Methods and Programs in Biomedicine, vol. 265, 2025.
K. Duquesne, “Toward video-based musculoskeletal modeling: advances in geometry, kinematics, and kinetics”, 2026.
K. Duquesne, Adris, M., Huysentruyt, R., Van Oevelen, A., Li, J., Sijbers, J., Van Paepegem, W., and Audenaert, E., “From Skin to Skeleton: A Statistical Shape Modelling Approach for Predicting Hand and Foot Bony Geometry”, Annals of Biomedical Engineering, vol. 54, no. 1, pp. 284-302, 2026.PDF icon Download paper (3.33 MB)
E
T. Elberfeld, De Beenhouwer, J., den Dekker, A. J., Heinzl, C., and Sijbers, J., “Parametric Reconstruction of Advanced Glass Fiber-reinforced Polymer Composites from X-ray Images”, 8th Conference on Industrial Computed Tomography. Wels, Austria, 2018.PDF icon Download paper (636.09 KB)
T. Elberfeld, De Beenhouwer, J., and Sijbers, J., “Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction”, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.PDF icon Download paper (5.15 MB)
T. Elberfeld, De Beenhouwer, J., den Dekker, A. J., Heinzl, C., and Sijbers, J., “Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study”, Journal of Nondestructive Evaluation, vol. 37, no. 62, pp. 1573-4862, 2018.
T. Elberfeld, Bazrafkan, S., De Beenhouwer, J., and Sijbers, J., “Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images”, 4th International Conference on Tomography of Materials & Structures. 2019.
T. Elberfeld, Fröhler, B., Heinzl, C., Sijbers, J., and De Beenhouwer, J., “cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites”, Nondestructive Testing and Evaluation, 2022.PDF icon Download paper (9.66 MB)
T. Elberfeld, “Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images”, 2023.PDF icon Download PhD thesis (35.09 MB)
L. Emsell, Leemans, A., Langan, C., Van Hecke, W., Barker, G. J., McCarthy, P., Jeurissen, B., Sijbers, J., Sunaert, S., Cannon, D. M., and McDonald, C., “Limbic and callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion MRI tractography study”, Biologicial Psychiatry, vol. 73, no. 2, pp. 194-201, 2013.
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J. Fatermans, Van Aert, S., and den Dekker, A. J., “Bayesian model-order selection in electron microscopy to detect atomic columns in noisy images”, RBSM 2016, Brussels, Belgium. p. 53, 2016.
J. Fatermans, den Dekker, A. J., Gauquelin, N., Verbeeck, J., and Van Aert, S., “Bayesian model selection for atom column detection from ABF-ADF STEM images”, Virtual Early Career EMC 2020 (online), Copenhagen, Denmark. 2020.
J. Fatermans, den Dekker, A. J., Müller-Caspary, K., Lobato, I., and Van Aert, S., “The maximum a posteriori probability rule to detect single atoms from low signal-to-noise ratio scanning transmission electron microscopy images”, IMC19, Sydney, Australia. 2018.
J. Fatermans, De Backer, A., den Dekker, A. J., and Van Aert, S., “Chapter Seven - Image-quality evaluation and model selection with maximum a posteriori probability”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
J. Fatermans, “Quantitative atom detection from atomic-resolution transmission electron microscopy images”, 2019.PDF icon Download thesis (29.73 MB)
J. Fatermans, den Dekker, A. J., Müller-Caspary, K., Lobato, I., O'Leary, C. M., Nellist, P. D., and Van Aert, S., “Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images”, Phys. Rev. Lett., vol. 121, p. 056101, 2018.
J. Fatermans, den Dekker, A. J., Müller-Caspary, K., Lobato, I., and Van Aert, S., “Bayesian analysis of noisy scanning transmission electron microscopy images for single atom detection”, SCANDEM 2018, Technical University of Denmark, Kgs. Lyngby, Denmark. p. 95, 2018.
J. Fatermans, den Dekker, A. J., and Van Aert, S., “Atom detection from electron microscopy images”, RBSM 2019, Louvain-la-Neuve, Belgium. p. 15, 2019.

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