Deep learning‐driven methods for fluorescence imaging denoising

Abstract Fluorescence imaging, serving as the primary imaging modality in modern life science research, faces a fundamental challenge in achieving high‐sensitivity imaging: optimizing the signal‐to‐noise ratio (SNR) under dynamic and complex experimental conditions. Due to autofluorescence, shot noise, and tissue scattering, this SNR deficiency disrupts subcellular morphometry, restricts recording reliability, and ultimately propagates artifacts in subsequent analysis. This review evaluates data‐driven deep learning denoising methods that overcome conventional limitations through effective feature extraction and nonlinear modeling. Focusing on fluorescence imaging acquisition under photon‐limited conditions, we delineate cutting‐edge architectures, including supervised learning, unsupervised learning, zero‐shot learning, and hybrid approaches. By producing higher‐fidelity image data, these denoising methods enhance the reliability of live‐cell imaging and the accuracy of neural mechanism analysis. This advancement provides a stronger foundation for elucidating dynamic biological processes and accelerating precision medicine.