Background2Background: a self-supervised background removal framework for high dimensional microscopy
High-dimensional microscopy captures features across space, time, and spectrum, but complex backgrounds obscure signals. We present Background2Background (B2B), a self-supervised background removal framework leveraging the backgrounds larger correlation relative to the signal. Local pixel shuffling disrupts signal correlations while preserving background structure, transforming signals into effective noise for Noise2Noise-based background removal. Across fluorescence, stimulated Raman scattering and mid-infrared photothermal imaging, B2B achieves over 40-fold background suppression and reveals hidden signatures.
| Authors | Guangrui Ding, Haonan Lin, Rong Tang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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