4 citations · 4 across the 2 of their papers we have counts for
8 papers
Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
Jakub Nalepa, Lukasz Tulczyjew, Michal Myller +1
Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands. Classificati…
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Jakub Nalepa, Pablo Ribalta Lorenzo, Michal Marcinkiewicz +8
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor. Although manual DCE biomarker extraction algorithms…
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images
Jakub Nalepa, Michal Myller, Michal Kawulok
Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery req…
On training deep networks for satellite image super-resolution
Michal Kawulok, Szymon Piechaczek, Krzysztof Hrynczenko +3
The capabilities of super-resolution reconstruction (SRR)---techniques for enhancing image spatial resolution---have been recently improved significantly by the use of deep convolu…
Hyperspectral Data Augmentation
Jakub Nalepa, Michal Myller, Michal Kawulok
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks. It plays a pivotal role in remote-sensing scenarios in which the a…
Deep Learning for Multiple-Image Super-Resolution
Michal Kawulok, Pawel Benecki, Szymon Piechaczek +3
Super-resolution reconstruction (SRR) is a process aimed at enhancing spatial resolution of images, either from a single observation, based on the learned relation between low and…