Dr. Jakub Nalepa Profile

Dr. Jakub Nalepa

Head of AI at KP Labs Sp. z o.o.

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Publications (8)

Proceedings Article | 19 October 2023 Presentation + Paper

Implementation of the Φsat-2 on board image processing chain

N. Melega, Bernardo Carnicero, N. Longepe, A. Paskeviciute, V. Marchese, Oriol Aragon, Irina Babkina, Alessandro Marin, Jakub Nalepa, Leonie Buckley, Giorgia Guerrisi, Sofia Oliviera, Hano Stein

Proceedings Volume 12729, 127290Z (2023) https://doi.org/10.1117/12.2679044

KEYWORDS: Artificial intelligence, Image processing, Image compression, Data modeling, Satellites, Image quality, Space operations, Satellite imaging

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Proceedings Article | 12 April 2021 Presentation + Paper

Segmentation of hyperspectral images using self-organizing maps

Pawel Sanocki, Michal Kawulok, Bogdan Smolka, Jakub Nalepa

Proceedings Volume 11736, 117360M (2021) https://doi.org/10.1117/12.2586236

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Proceedings Article | 14 May 2019 Presentation + Paper

Robust switching technique of impulsive noise removal in color digital images

Bogdan Smolka, Boguslaw Cyganek, Michal Kawulok, Jakub Nalepa

Proceedings Volume 10996, 109960M (2019) https://doi.org/10.1117/12.2519301

KEYWORDS: Digital filtering, Image filtering, Image processing, Switching, Optical filters, Denoising, Image enhancement, Distance measurement, Quality measurement, Video

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In this paper a novel method of impulsive noise removal in color images is presented. The proposed filtering design is based on a new measure of pixel similarity, which takes into account the structure of the local neighborhood of the pixels being compared. Thus, the new distance measure can be regarded as an extension of the reachability distance used in the construction of the local outlier factor, widely used in the big data analysis. Using the new similarity measure, an extension of the classic Vector Median Filter (VMF) has been developed. The new filter is extremely robust to outliers introduced by the impulsive noise, retains details and has the unique ability to sharpen image edges. Using the structure of the developed filter, a new impulse detector has been constructed. The cumulated sum of smallest reachability distances in the filtering window serves as a robust measure of pixel outlyingness. In this way, a pixel will be treated as corrupted if a predefined threshold is exceeded and will be replaced by the average of pixels which were found to belong to the original, pristine image; otherwise the processed pixel will be retained. This structure is similar to the Fast Averaging Peer Group Filter, however the incorporation of the reachability measure makes this technique more robust. The new filtering design can be applied in real time scenario, as its computational efficiency is comparable with the standard VMF, which is fast enough to be used for the enhancement of video sequences. The new filter operates in a 3×3 filtering window, however the information acquired from a larger window is processed. The source of additional information is the local neighborhood of pixels, which is used for the determination of the novel reachability measure. The experiments performed on a large database of color images show that the new filter surpasses existing designs especially in the case of highly polluted images. The robust reachability measure assures that the clusters of impulses are being removed, as not only the pixels, but also their neighborhoods are considered. The novel measure of dissimilarity can be also used in other tasks whose main goal is the detection of outliers.

Proceedings Article | 14 May 2019 Presentation + Paper

On data augmentation for segmenting hyperspectral images

Jakub Nalepa, Michal Myller, Michal Kawulok, Bogdan Smolka

Proceedings Volume 10996, 1099609 (2019) https://doi.org/10.1117/12.2519517

KEYWORDS: Data modeling, Hyperspectral imaging, Statistical modeling, Image segmentation, Principal component analysis, Neural networks, Convolutional neural networks, Statistical analysis

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Proceedings Article | 14 May 2019 Presentation + Paper

Deep learning for fast super-resolution reconstruction from multiple images

Michal Kawulok, Pawel Benecki, Krzysztof Hrynczenko, Daniel Kostrzewa, Szymon Piechaczek, Jakub Nalepa, Bogdan Smolka

Proceedings Volume 10996, 109960B (2019) https://doi.org/10.1117/12.2519579

KEYWORDS: Image processing, Super resolution, Image fusion, Convolution, Image registration, Image filtering, Convolutional neural networks

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Proceedings Article | 14 May 2018 Presentation + Paper

Robust enhancement technique for color images corrupted by impulsive noise

Bogdan Smolka, Jakub Nalepa, Michal Kawulok, Boguslaw Cyganek

Proceedings Volume 10670, 1067003 (2018) https://doi.org/10.1117/12.2303717

KEYWORDS: Image filtering, Image enhancement, Digital filtering, Denoising, Gaussian filters, Optical filters, Image processing, Contamination, Roads, Image quality

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Proceedings Article | 14 May 2018 Presentation + Paper

Real-time lung segmentation from whole-body CT scans using Adaptive Vision Studio: a visual programming software suite

Jakub Nalepa, Michal Czardybon, Maksym Walczak

Proceedings Volume 10670, 106700C (2018) https://doi.org/10.1117/12.2303955

KEYWORDS: Lung, Image segmentation, Computed tomography, Visualization, Image processing algorithms and systems, 3D image processing, Computer programming, Medical imaging, Image analysis, Image processing

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Proceedings Article | 14 May 2018 Presentation + Paper

Evolutionary cortical surface segmentation

Maksym Walczak, Jakub Nalepa, Michal Kawulok, Wojciech Dudzik, Bogdan Smolka

Proceedings Volume 10670, 106700D (2018) https://doi.org/10.1117/12.2304549

KEYWORDS: Brain, Image segmentation, Neuroimaging, Magnetic resonance imaging, Algorithm development, Image processing, Image processing algorithms and systems, Anisotropic diffusion, Genetic algorithms, Evolutionary algorithms

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Showing 5 of 8 publications

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