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A. Pizurica, Scheunders, P., and Philips, W., “Multiresolution multispectral image denoising based on probability of presence of features of interest”, in Advanced Concepts in Image and Vision Systems, Brussels, 2004, pp. 357-364.
A. Pizurica, Huysmans, B., Scheunders, P., and Philips, W., “Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context”, in IEEE International Geoscience and Remote Sensing Symp. IGARSS 2005, Seoul, Korea, 25-29 July, Seoul, Korea, 2005, vol. 6, pp. 4260-4263.
A. Pizurica, Philips, W., and Scheunders, P., “Wavelet domain denoising of single-band and multi-band images adapted to the probability of the presence of features of interes”, in SPIE Wavelets XI, San Diego, California, USA, 31 July – 4 Aug, 2005, vol. 5914, pp. 508-521.
L. Plantagie, Palenstijn, W. J., Sijbers, J., and Batenburg, K. J., “Spatial Variations in Reconstruction Methods for CT”, in The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting), 2012, pp. 170-173.
L. Plantagie, Van Aarle, W., Batenburg, K. J., and Sijbers, J., “Filtered backprojection using algebraic filters; Application to biomedical micro-CT data”, in International Symposium on Biomedical Imaging (ISBI), 2015, pp. 1596-1599.
D. H. J. Poot, Van Meir, V., and Sijbers, J., “General and Efficient Super-Resolution method for Multi-Slice MRI”, in Medical Image Computing and Computer Assisted Intervention, 2010, vol. 13, no. 1, pp. 615-622.PDF icon Download paper (229.84 KB)
D. H. J. Poot, Sijbers, J., den Dekker, A. J., and Bos, R., “Estimation of the noise variance from the background histogram mode of an MR image”, in Proceedings of SPS-DARTS 2006 (The second annual IEEE BENELUX/DSP Valley Signal Processing Symposium), Antwerp, Belgium, 2006, pp. 159-162.
D. H. J. Poot, Sijbers, J., and den Dekker, A. J., “An exploration of spatial similarities in temporal noise spectra in fMRI measurements”, in Proceedings of SPIE Medical Imaging 2008, San Diego, CA, USA, 2008, vol. 6914, p. 69142.
D. H. J. Poot, Sijbers, J., and den Dekker, A. J., “Optimizing the Diffusion Kurtosis imaging acquisition”, in European Society for Magnetic Resonance in Medicine and Biology, Valencia, Spain, 2008.
D. H. J. Poot, Sijbers, J., den Dekker, A. J., and Bos, R., “Estimation of the noise variance from the background histogram mode of an MR image”, in Proceedings of the 25th Benelux Meeting on Systems and Control, Heeze, The Netherlands, 2006.
D. H. J. Poot, Sijbers, J., den Dekker, A. J., and Pintjens, W., “Automatic estimation of the noise variance from the histogram of a magnetic resonance image”, in IEEE/EBMS Benelux Symposium proceedings, 2006, pp. 135-138.
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.
A. Presenti, Liang, Z., Alves Pereira, L. F., Sijbers, J., and De Beenhouwer, J., “CNN-based pose estimation from a single X-ray projection for 3D inspection of manufactured objects”, in 11th Conference on Industrial Computed Tomography, 2022.
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)
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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)
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.
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.
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)

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