A computational super-resolution framework for multidimensional fluorescence imaging.

A computational framework using deblurring by pixel reassignment enhances multidimensional fluorescence imaging resolution, revealing fine details from single captures.

• Why it matters: Capturing the heterogeneity of biological processes at the nanoscale is limited by conventional microscopy and post hoc data merging, risking signal loss and mismatches.
• What they did: The approach processes high-dimensional tensor data combining spatial, spectral, and lifetime information, enabling super-resolution from standard fluorescence images with a single acquisition.
• The result: This method improves spatial resolution and preserves molecular signals, facilitating detailed, quantitative insights into complex biological systems at the single-molecule level.