4 papers
Going beyond p-convolutions to learn grayscale morphological operators
Alexandre Kirszenberg, Guillaume Tochon, Elodie Puybareau +1
Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with eros…
Learning Sentinel-2 Spectral Dynamics for Long-Run Predictions Using Residual Neural Networks
Joaquim Estopinan, Guillaume Tochon, Lucas Drumetz
Making the most of multispectral image time-series is a promising but still relatively under-explored research direction because of the complexity of jointly analyzing spatial, spe…
Experimental digital Gabor hologram rendering by a model-trained convolutional neural network
J. Rivet, A. Taliercio, C. Fang +4
Digital hologram rendering can be performed by a convolutional neural network, trained with image pairs calculated by numerical wave propagation from sparse generating images. 512-…
Learning Endmember Dynamics in Multitemporal Hyperspectral Data Using a State-Space Model Formulation
Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon +1
Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (…