Publications

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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.
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)
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)
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.
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, den Dekker, A. J., and Sijbers, J., “Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction”, IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1041 - 1053, 2017.PDF icon Download paper (3.72 MB)
G. Ramos-Llordén, den Dekker, A. J., and Sijbers, J., “Joint motion correction and estimation for T1 mapping: proof of concept”, Medical Imaging Summer School 2014, Favignana, Italy. 2014.
G. Ramos-Llordén, Beirinckx, Q., den Dekker, A. J., and Sijbers, J., “An educational presentation on accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation”, 10th Annual Meeting of the ISMRM Benelux Chapter. Antwerp, Belgium, 2018.
G. Ramos-Llordén and Sijbers, J., “Misalignment correction for T1 maps using a maximum likelihood estimator approach”, Imaging the brain at different scales: How to integrate multi-scale structural information?, Antwerp, Belgium, 2 Sep - 6 Sep, 2013. 2013.
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping”, 23rd Annual meeting of the ISMRM, Toronto, Canada., vol. 23. p. 447, 2015.PDF icon Download abstract (910.6 KB)
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping”, 7th meeting of the ISMRM Benelux Chapter, Gent, Belgium, January. 2015.
G. Ramos-Llordén, Beirinckx, Q., den Dekker, A. J., and Sijbers, J., “Accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation”, Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), 26th Annual Meeting. Paris, France, p. 5664, 2018.
G. Ramos-Llordén, Vegas-Sánchez-Ferrero, G., Björk, M., Vanhevel, F., Parizel, P. M., Estépar, R. San José, den Dekker, A. J., and Sijbers, J., “NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping”, IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2414 - 2427, 2018.PDF icon Download paper (3.3 MB)
G. Ramos-Llordén, den Dekker, A. J., Bladt, P., Cuyt, A., and Sijbers, J., “Statistically optimal separation of multi-component MR signals with a Majorize-Minimize approach: application to MWF estimation”, 34th annual scientific meeting of the ESMRMB. 2017.

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