Single-sample image-fusion upsampling of fluorescence lifetime images
This addresses a bottleneck in FLIM for biological imaging, offering a computational solution to improve resolution without hardware changes, though it appears incremental as it builds on existing fusion concepts.
The authors tackled the problem of low resolution in fluorescence lifetime imaging microscopy (FLIM) at high speeds by developing SiSIFUS, a data-fusion method that combines low-resolution time-resolved and high-resolution intensity measurements to achieve super-resolution, delivering enhanced images compared to standard bilinear interpolation.
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 (SiSIFUS), 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 dependencies 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 SiSIFUS can be applied to other image super-resolution problems where two different datasets are available.