Abstract:High-throughput biological imaging is often constrained by a trade-off between acquisition speed and image quality. Fast imaging modalities, such as wide-field fluorescence microscopy, enable large-scale data acquisition but suffer from reduced contrast and resolution, whereas high-resolution techniques, including confocal microscopy or single-molecule localization microscopy-based super-resolution techniques, provide superior image quality at the cost of throughput and instrument time. Here, we present a deep learning-based approach for modality transfer across independent microscopes, enabling the transformation of low-quality images acquired on fast systems into high-quality representations comparable to those obtained using advanced imaging platforms. To achieve this, we employ a generative adversarial network (GAN)-based model trained on paired datasets acquired on physically separate wide-field and confocal microscopes, demonstrating that image quality can be reliably transferred between independent instruments. Quantitative evaluation shows substantial improvement in structural similarity and signal fidelity, with median SSIM and PSNR of 0.94 and 31.87, respectively, compared to 0.83 and 21.48 for the original wide-field images. These results indicate that key structural features can be recovered with high accuracy. Importantly, this approach enables a workflow in which high-throughput imaging can be performed on fast, accessible microscopy systems while preserving the ability to computationally recover high-quality structural information. High-resolution microscopy can then be reserved for targeted validation, reducing acquisition time and improving overall experimental efficiency. Together, our results establish deep learning-enabled modality transfer as a practical strategy for bridging independent microscopy systems and supporting scalable, high-content imaging workflows.
| Comments: | 17 Pages, 5 Figures, 1 Table, 4 pages Supplementary Materials |
| Subjects: | Image and Video Processing (eess.IV); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2403.18026 [eess.IV] |
| (or arXiv:2403.18026v2 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2403.18026 arXiv-issued DOI via DataCite |
From: Zbigniew Baster [view email]
[v1]
Tue, 26 Mar 2024 18:23:31 UTC (1,548 KB)
[v2]
Fri, 17 Apr 2026 14:53:28 UTC (1,849 KB)
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