Upconversion photoluminescence lifetime imaging has emerged as a transformative analytical tool by leveraging the temporal signatures of luminescent probes to resolve molecular interactions. While compressed ultrafast photography offers distinct advantages for high-speed imaging

its utility in quantitative applications

such as food safety

remains constrained by reconstruction artifacts

particularly spatial distortion and edge detail loss. To address these limitations

we present a method of single-shot compressed upconversion photoluminescence lifetime imaging (sCUPLI) empowered by a multi-prior physics-enhanced neural (mPEN) network. This framework synergizes physical models of photoluminescence dynamics and imaging processing

subpixel information loss of the compressed encoding

extended sampling priors

sparsity constraints

and deep learning to achieve precise quantification of upconversion photoluminescence lifetimes. To demonstrate its potential in beverage additive detection

we apply mPEN–sCUPLI to analyze synthetic colorants using rare-earth-doped upconversion nanoprobes. The system excels at resolving subtle optical heterogeneities in beverage matrices

achieving superior spatiotemporal resolution and artifact suppression compared to conventional methods. It attains a spatial resolution of 90.5 lp/mm at a frame rate of 33

000 fps

achieving a spatial resolution improvement of approximately 3.56 times. Quantitative analysis shows that mPEN–sCUPLI reconstruction improves the average peak signal-to-noise ratio by 4 dB and enhances the sharpness and fidelity of imaging by 1.85 times. Given its ability to achieve high-fidelity and high-throughput photoluminescence lifetime imaging from single-shot 2-dimensional measurements

the proposed mPEN–sCUPLI approach is expected to be widely adopted in food safety detection applications.