Volumetric localization microscopy with deep learning.

ABSTRACT: Super-resolution microscopy, particularly localization-based methods, necessitates careful balancing of optical complexity, computational demands, and user accessibility. Conventional strategies typically adopt either deterministic or learning-based approaches, overlooking opportunities to leverage their synergistic strengths. In this work, we introduce volumetric localization microscopy (VLM) with deep learning, a super-resolution methodology that integrates instrumental and algorithmic advancements for high-fidelity 3D single-molecule imaging. VLM employs a wavefront-optimized light-field configuration to capture single-molecule data, while a cascaded neural network reconstructs 3D volumes and extracts molecular coordinates at a 10 nm lateral and 25 nm axial localization precision with e

SUBMITTER: Han K 

PROVIDER: S-EPMC12686016 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature