Spatial redundancy transformer for self-supervised fluorescence image denoising.
Nat Comput Sci
; 3(12): 1067-1080, 2023 Dec.
Article
en En
| MEDLINE
| ID: mdl-38177722
ABSTRACT
Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis of biological phenomena. However, the inevitable noise poses a formidable challenge to imaging sensitivity. Here we provide the spatial redundancy denoising transformer (SRDTrans) to remove noise from fluorescence images in a self-supervised manner. First, a sampling strategy based on spatial redundancy is proposed to extract adjacent orthogonal training pairs, which eliminates the dependence on high imaging speed. Second, we designed a lightweight spatiotemporal transformer architecture to capture long-range dependencies and high-resolution features at low computational cost. SRDTrans can restore high-frequency information without producing oversmoothed structures and distorted fluorescence traces. Finally, we demonstrate the state-of-the-art denoising performance of SRDTrans on single-molecule localization microscopy and two-photon volumetric calcium imaging. SRDTrans does not contain any assumptions about the imaging process and the sample, thus can be easily extended to various imaging modalities and biological applications.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Calcio de la Dieta
/
Automanejo
Límite:
Humans
Idioma:
En
Año:
2023
Tipo del documento:
Article