ArticleScience advances2024
Single-sample image-fusion upsampling of fluorescence lifetime images.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Emerging trends of fluorescence lifetime imaging microscopy (FLIM): advances, challenges, and prospects.Biophysics reports · 2026Article
- Fluorescence-lifetime optical electrophysiology in contracting cardiomyocytes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Chemical fluorophores for fluorescence lifetime imaging.Chemical Society reviews · 2026Review
- Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas.Nature communications · 2025Article
- Insights into metabolic changes during epidermal differentiation as revealed by multiphoton microscopy with fluorescence lifetime imaging.Scientific reports · 2025Article
- Real-time wide-field fluorescence lifetime imaging via single-snapshot acquisition for biomedical applications.PhotoniX · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fluorescence lifetime imaging microscopy (FLIM) provides detailed information about molecular interactions and biological processes. A major bottleneck for FLIM is image resolution at high acquisition speeds due to the engineering and signal-processing limitations of time-resolved imaging technology. Here, we present single-sample image-fusion upsampling, a data-fusion approach to computational FLIM super-resolution that combines measurements from a low-resolution time-resolved detector (that measures photon arrival time) and a high-resolution camera (that measures intensity only). To solve this otherwise ill-posed inverse retrieval problem, we introduce statistically informed priors that encode local and global correlations between the two "single-sample" measurements. This bypasses the risk of out-of-distribution hallucination as in traditional data-driven approaches and delivers enhanced images compared, for example, to standard bilinear interpolation. The general approach laid out by single-sample image-fusion upsampling can be applied to other image super-resolution problems where two different datasets are available.
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Registered trials
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