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
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.