  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.
