Deep learning enables contrast-robust super-resolution reconstruction in structured illumination microscopy.
Opt Express
; 32(3): 3316-3328, 2024 Jan 29.
Article
em En
| MEDLINE
| ID: mdl-38297556
ABSTRACT
Structured illumination microscopy (SIM) is a powerful technique for super-resolution (SR) image reconstruction. However, conventional SIM methods require high-contrast illumination patterns, which necessitate precision optics and highly stable light sources. To overcome these challenges, we propose a new method called contrast-robust structured illumination microscopy (CR-SIM). CR-SIM employs a deep residual neural network to enhance the quality of SIM imaging, particularly in scenarios involving low-contrast illumination stripes. The key contribution of this study is the achievement of reliable SR image reconstruction even in suboptimal illumination contrast conditions. The results of our study will benefit various scientific disciplines.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Opt Express
Assunto da revista:
OFTALMOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article