Approaching maximum resolution in structured illumination microscopy via accurate noise modeling.

ABSTRACT: Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximu

SUBMITTER: Saurabh A 

PROVIDER: S-EPMC11785531 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature