mlsanityData to 14 Oct 2026

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

arXiv 2306.11848 ↗First posted 20 Jun 2023cs.CV cs.LG eess.IVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 20 Jun 2023.

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14 of 33 references.

References · 14

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2 more
  1. 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 429
  2. 2023Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosiscited by 1

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  1. 2025Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputscited by 1