AI-Augmented Pollen Recognition in Optical and Holographic Microscopy for Veterinary Imaging

• URL: http://arxiv.org/abs/2512.12101v1
• Date: Sat, 13 Dec 2025 00:26:45 GMT
• Title: AI-Augmented Pollen Recognition in Optical and Holographic Microscopy for Veterinary Imaging
• Authors: Swarn S. Warshaneyan, Maksims Ivanovs, Blaž Cugmas, Inese Bērziņa, Laura Goldberga, Mindaugas Tamosiunas, Roberts Kadiķis,
• Abstract summary: We present a comprehensive study on fully automated pollen recognition across conventional optical and digital in-line holographic microscopy (DIHM) images.<n>Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substantial divergence from bright-field appearances.<n>We employ a Wasserstein GAN with spectral normalization to create synthetic DIHM images, yielding an FID score of 58.246.
• Score: 0.1563562770254469
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a comprehensive study on fully automated pollen recognition across both conventional optical and digital in-line holographic microscopy (DIHM) images of sample slides. Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substantial divergence from bright-field appearances. We establish the performance baseline by training YOLOv8s for object detection and MobileNetV3L for classification on a dual-modality dataset of automatically annotated optical and affinely aligned DIHM images. On optical data, detection mAP50 reaches 91.3% and classification accuracy reaches 97%, whereas on DIHM data, we achieve only 8.15% for detection mAP50 and 50% for classification accuracy. Expanding the bounding boxes of pollens in DIHM images over those acquired in aligned optical images achieves 13.3% for detection mAP50 and 54% for classification accuracy. To improve object detection in DIHM images, we employ a Wasserstein GAN with spectral normalization (WGAN-SN) to create synthetic DIHM images, yielding an FID score of 58.246. Mixing real-world and synthetic data at the 1.0 : 1.5 ratio for DIHM images improves object detection up to 15.4%. These results demonstrate that GAN-based augmentation can reduce the performance divide, bringing fully automated DIHM workflows for veterinary imaging a small but important step closer to practice.

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