Using Super-Resolution for Enhancing Visual Perception and Segmentation Performance in Veterinary Cytology

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) metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state-of-the-art in cytology image analysis.

Keywords

• super image resolution
• computer vision
• deep learning
• cytology
• medical imaging
• semantic segmentation