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G. Ramos-Llordén, Segers, H., Palenstijn, W. J., den Dekker, A. J., and Sijbers, J., “Partial discreteness: a new type of prior knowledge for MRI reconstruction”, 23rd Annual meeting of the ISMRM, Toronto, Canada., vol. 23. p. 3417, 2015.PDF icon Download abstract (1.14 MB)
G. Ramos-Llordén, den Dekker, A. J., Björk, M., Verhoye, M., and Sijbers, J., “NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals”, 24th Annual Meeting of the ISMRM, Singapore. 2016.
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.
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Jeurissen, B., Vanhevel, F., Van Audekerke, J., Verhoye, M., and Sijbers, J., “A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping”, IEEE Transactions on Medical Imaging, vol. 36, no. 2, pp. 433 - 446, 2017.
B. Rasti and Koirala, B., “SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network”, IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.PDF icon ieee_grsl_sundip.pdf (2.34 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, no. 1728, 2020.PDF icon remotesensing-12-01728.pdf (2.27 MB)
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., Scheunders, P., and Ghamisi, P., “UnDIP: hyperspectral unmixing using deep image prior”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2021.PDF icon manuscript.pdf (13 MB)
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., 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, Scheunders, P., Ghesami, P., Licciardi, G., and Chanussot, J., “Noise reduction in hyperspectral imagery: overview and application”, Remote Sensing , vol. 10, no. 3, p. 482, 2018.
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 5522815, 2022.PDF icon misicnet_ieee_tgrs_author_version.pdf (5.57 MB)
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.PDF icon misicnet_ieee_tgrs_author_version.pdf (5.52 MB)
B. Rasti, Koirala, B., and Scheunders, P., “HapkeCNN: Blind nonlinear unmixing for intimate mixtures using Hapke model and convolutional neural network”, IEEE Transactions on Geoscience and Remote Sensing, 2022.PDF icon hapke_cnn.pdf (8.14 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., 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)
B. Rasti, Koirala, B., and Scheunders, P., “HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.PDF icon hapkecnnrg.pdf (8.23 MB)
A. J. Rebelo, Scheunders, P., Esler, K. J., and Meire, P., “Evaluating palmiet wetland decline: a comparison of three methods”, Remote Sensing Applications: Society and Environment, vol. 8, pp. 212-223, 2017.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Constrained spherical deconvolution based tractography and cognition in Alzheimer’s disease”, Congress of the International Society for Vascular, Cognitive and Behavioural Disorders, vol. 5. Lille, France, 2011.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Constrained spherical deconvolution based tractography and cognition in Alzheimer’s disease”, International Conference on Alzheimer’s Disease. Paris, France, 2011.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Constrained spherical deconvolution based tractography and cognition in Alzheimer’s disease”, Human Brain Mapping. Québec, Canada, 2011.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Improved sensitivity to cerebral white matter abnormalities in Alzheimer's disease with spherical deconvolution based tractography.”, PloS one, vol. 7, no. 8, p. e44074, 2012.
P. Reischig, King, A., Nervo, L., Viganó, N., Guilhem, Y., Palenstijn, W. J., Batenburg, K. J., Preuss, M., and Ludwig, W., “Advances in X-ray diffraction contrast tomography: flexibility in the setup geometry and application to multiphase materials”, Journal of Applied Crystallography, vol. 46, no. 2, pp. 297 - 311, 2013.
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.
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)

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