- Abstract: In this work we combine a mixed-scale dense convolutional network and a structure preserving loss function, to increase the in-plane resolution of MRI images with sub-millimeter resolution. Despite having 20 times fewer parameters than SRCNN, this architecture can reconstruct high-resolution images in comparable quality and learns to better preserve high-frequency details than networks trained with L 2 -loss. Preliminary experiments show that the architecture in general is working well and comparable to the well known SRCNN without any optimization of hyper-parameters.
- Keywords: Super-Resolution, Deep Learning, Convolutional Neural Networks, MRI
- Author Affiliation: Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany