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
Unverified — Be the first to reproduce this paper.
ReproduceAbstract
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.