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C. C. Sabino, Andrade, L. S., Tsang, I. R., Cavalcanti, G. D. C., Tsang, I. J., and Sijbers, J., “Motion Compensation Techniques in Permutation-Based Video Encryption”, in Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Washington, DC, USA, 2013, pp. 1578–1581.
E. Ribeiro Sabidussi, Caan, M., Bazrafkan, S., den Dekker, A. J., Sijbers, J., Niessen, W. J., and Poot, D. H. J., “Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry”, in MIDL 2021 - Medical Imaging with Deep Learning, 2021.
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T. Roelandts, Batenburg, K. J., and Sijbers, J., “Localizing DART using the Reconstructed Residual Error”, in 1st International Conference on Tomography of Materials and Structures (ICTMS), Ghent, Belgium, 2013, vol. Book of Abstracts: Talks, pp. 113-116.PDF icon Download full paper (491.53 KB)
T. Roelandts, Batenburg, K. J., and Sijbers, J., “PDART: A Partially Discrete Algorithm for the Reconstruction of Dense Particles”, in 11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D), Potsdam, Germany, 2011, pp. 448-451.PDF icon Download full paper (1.74 MB)
T. Roelandts, Batenburg, K. J., and Sijbers, J., “Visualizing the Segmentation Error of a Tomogram using the Residual Projection Error”, in The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting), Salt Lake City, UT, USA, 2012, pp. 293-296.
H. Rezaei, Karami, A., and Scheunders, P., “Hyperspectral and multispectral image fusion based on spectral matching in the shearlet domain”, in IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27, 2018.
J. Renders, De Beenhouwer, J., and Sijbers, J., “Mesh-based reconstruction of dynamic foam images using X-ray CT”, in International Conference on 3D Vision (3DV2021), 2021, pp. 1312-1320.
J. Renders, Sijbers, J., and De Beenhouwer, J., “Adjoint pairs of image warping operators for motion modeling in 4D-CT”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.PDF icon Download paper (617.11 KB)
J. Renders, Shafieizargar, B., Verhoye, M., De Beenhouwer, J., den Dekker, A. J., and Sijbers, J., “DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data”, in IEEE International Symposium on Biomedical Imaging, 2023, pp. 1-4.
B. Rasti and Koirala, B., “Blind Nonlinear Unmixing For Intimate Mixtures Using Hapke Model And CNN”, in Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2022, pp. 1-5.PDF icon whispers_2022_hapkecnn.pdf (1.93 MB)
B. Rasti, Koirala, B., and Scheunders, P., “Sparse Unmixing using Deep Convolutional Networks”, in IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 24-27.PDF icon suncnn_igarss2022.pdf (1.03 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “Spectral Unmixing Using Deep Convolutional Encoder-Decoder”, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3829-3832.PDF icon igarss2021.pdf (659.27 KB)
B. Rasti, Koirala, B., Scheunders, P., Ghamisi, P., and Gloaguen, R., “BOOSTING HYPERSPECTRAL IMAGE UNMIXING USING DENOISING: FOUR SCENARIOS”, in IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021.
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “Deep Blind Unmixing using Minimum Simplex Convolutional Network”, in IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 28-31.PDF icon misicnet_igarss2022.pdf (1.29 MB)
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach”, in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 3160-3164.
G. Ramos-Llordén, Segers, H., Palenstijn, W. J., den Dekker, A. J., and Sijbers, J., “Partially discrete magnetic resonance tomography”, in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 1653-1657.
J. Rajan, Poot, D. H. J., Juntu, J., and Sijbers, J., “Segmentation Based Noise Variance Estimation from Background MRI Data”, in ICIAR , Porto, Portugal, 2010, vol. 6111, pp. 62-70.
J. Rajan, Jeurissen, B., Sijbers, J., and Kannan, K., “Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion”, in 13th International Machine Vision and Image Processing Conference, Dublin, Ireland, 2009, pp. 123-127.
J. Rajan, den Dekker, A. J., Juntu, J., and Sijbers, J., “A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images”, in 5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings, 2013, Lecture Notes in Computer Science., vol. 8251.
J. Rajan, Verhoye, M., and Sijbers, J., “A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood”, in SPIE Medical Imaging, 2011, vol. 7962.
J. Rajan, Van Audekerke, J., Van Der Linden, A., Verhoye, M., and Sijbers, J., “An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images”, in IEEE International Symposium on Biomedical Imaging (ISBI), 2012.PDF icon Download full paper (307.46 KB)
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A. Presenti, Liang, Z., Alves Pereira, L. F., Sijbers, J., and De Beenhouwer, J., “CNN-based Pose Estimation of Manufactured Objects During Inline X-ray Inspection”, in 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI), 2021.
A. Presenti, Sijbers, J., den Dekker, A. J., and De Beenhouwer, J., “CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019, pp. 1-5.PDF icon ict2019_full_paper_55.pdf (216.2 KB)
A. Presenti, Sijbers, J., and De Beenhouwer, J., “Dynamic angle selection for few-view X-ray inspection of CAD based objects”, in Proc. SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.
A. Presenti, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., “Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.

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