Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy.

ABSTRACT: One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions (i.e., resolution anisotropy), which severely deteriorates the quality, reconstruction, and analysis of 3D volume images. By leveraging the natural anisotropy, we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets. By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods.

SUBMITTER: Ning K 

PROVIDER: S-EPMC10462670 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature