# Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology Source: [Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:26:24.585Z Authors: Jakub Caputa, Maciej Wielgosz, Daria {\\L}ukasik, Pawe{\\l} Russek, Jakub Grzeszczyk, Micha{\\l} Karwatowski, Szymon Mazurek, Rafa{\\l} Fr\\k{a}czek, Anna \\'Smiech, Ernest Jamro, Sebastian Koryciak, Agnieszka D\\k{a}browska-Boruch, Marcin Pietro\\'n, Kazimierz Wiatr Published: 2023-06-20T18:59:27.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on NYU Depth V2 (test)](): Semantic segmentation; NYU Depth V2 (test); mIoU: 11.13. ## Original sources and implementation [Original paper]() [PDF]()