Physics-Informed Deep Learning Model for Cross-Modality Super-Resolution in Fluorescence Microscopy

• URL: http://arxiv.org/abs/2607.21190v1
• Date: Thu, 23 Jul 2026 11:16:26 GMT
• Title: Physics-Informed Deep Learning Model for Cross-Modality Super-Resolution in Fluorescence Microscopy
• Abstract summary: Cross-modality image translation offers a route to super-resolution fluorescence microscopy from low-resolution images.<n>We propose a physics-informed generative adversarial network for confocal-to-STED image translation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-modality image translation offers a route to super-resolution fluorescence microscopy from low-resolution images while reducing phototoxicity and instrumentation demands. However, purely data-driven models can produce visually plausible outputs that are inconsistent with optical image formation. Here, we propose a physics-informed generative adversarial network for confocal-to-STED image translation that incorporates microscope-specific point spread function information into the training objective. Simulated and experimentally measured PSFs were evaluated using a limited paired confocal-STED dataset of TOM20-labeled mitochondria in human primary M2 macrophages acquired across different experimental days. Performance was assessed using reference-based and non-reference-based image-quality metrics, together with complementary frequency- and distribution-sensitive analyses. The no-reference metrics probed physics-relevant image properties, including spatial-frequency content, contrast, and signal-to-noise behavior. PSF-guided models improved structural fidelity, reduced local deviations, and achieved closer agreement with STED references than non-PSF baselines, particularly in frequency-domain analyses. These results demonstrate that optical priors can improve the structural fidelity and physical plausibility of generative microscopy models for cross-modality super-resolution imaging.

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