Reconstructing interpretable features in computational super-resolution microscopy via regularized latent search
Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2024-01-01
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| Series: | Biological Imaging |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2633903X24000084/type/journal_article |