126 citations · 433 across the 16 of their papers we have counts for
17 papers
Task-driven real-world super-resolution of document scans
Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel +2
Single-image super-resolution refers to the reconstruction of a high-resolution image from a single low-resolution observation. Although recent deep learning-based methods have dem…
Toward task-driven satellite image super-resolution
Maciej Ziaja, Pawel Kowaleczko, Daniel Kostrzewa +2
Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations. State-of-the-art approaches underpinned with deep learning allow for obtaining…
Task-driven single-image super-resolution reconstruction of document scans
Maciej Zyrek, Michal Kawulok
Super-resolution reconstruction is aimed at generating images of high spatial resolution from low-resolution observations. State-of-the-art super-resolution techniques underpinned…
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok +4
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can…
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
Tomasz Tarasiewicz, Jakub Nalepa, Reuben A. Farrugia +4
Multispectral Sentinel-2 images are a valuable source of Earth observation data, however spatial resolution of their spectral bands limited to 10 m, 20 m, and 60 m ground sampling…
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok +3
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can…
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution
Pawel Kowaleczko, Tomasz Tarasiewicz, Maciej Ziaja +4
Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. T…
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé +2
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic u…
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé +2
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire m…
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…
Validating Hyperspectral Image Segmentation
Jakub Nalepa, Michal Myller, Michal Kawulok
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being…
Real-Time Hand Shape Classification
Jakub Nalepa, Michal Kawulok
The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and app…