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arXiv · 2306.11848

Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology

Jakub Caputa·Maciej Wielgosz·Daria Łukasik·Paweł Russek·Jakub Grzeszczyk·Michał Karwatowski·Szymon Mazurek·Rafał Frączek·Anna Śmiech·Ernest Jamro·Sebastian Koryciak·Agnieszka Dąbrowska-Boruch·Marcin Pietroń·Kazimierz Wiatr
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Abstract

The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental results demonstrate that the integration of SR techniques into the segmentation pipeline can lead to a significant improvement of up to 25% in the mean average precision (mAP) segmentation metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state of the art in cytology image analysis.

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Jakub Caputa, Maciej Wielgosz, Daria Łukasik, Paweł Russek, Jakub Grzeszczyk, Michał Karwatowski, Szymon Mazurek, Rafał Frączek, Anna Śmiech, Ernest Jamro, Sebastian Koryciak, Agnieszka Dąbrowska-Boruch, Marcin Pietroń, Kazimierz Wiatr. 2023-06-20. Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology. https://arxiv.org/abs/2306.11848

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