Diffusion-based self-supervised adaptive denoising for live-cell fluorescence microscopy

Abstract

To mitigate phototoxicity, photobleaching, and motion blur, long-term live-cell fluorescence imaging is typically performed with low excitation intensity and short exposure times, resulting in extremely low and progressively decaying signal-to-noise ratio (SNR). Meanwhile, the inherent irreproducibility of biological processes makes it exceedingly difficult to acquire ground-truth images. Under these conditions, existing denoising methods often suffer from structural detail loss. To address these challenges, we propose diffusion-guided unsupervised adaptive learning (DUAL), a two-stage self-supervised denoising framework that integrates regression-based and generative models. In the first stage, a regression module produces a stable structural estimate that preserves the underlying biological morphology. In the second stage, a conditional diffusion model restores high-frequency details under the constraint of this estimate, while effectively suppressing structural hallucinations. To further accommodate time-varying SNR, we introduce an adaptive diffusion inversion strategy that dynamically aligns the denoising strength with the degradation level of the input. In addition, temporal conditioning is incorporated to enhance cross-frame consistency. Extensive experiments on synthetic and real dynamic imaging datasets demonstrate that DUAL achieves superior denoising performance. It effectively improves noise suppression, structural preservation, and temporal consistency across diverse fluorescence imaging scenarios with low and time-varying SNR, providing a powerful computational solution for long-term fluorescence microscopy enhancement.