Structured illumination microscopy (SIM) reconstructs optically-sectioned images of a sample from multiple spatially-patterned wide-field images, but the traditional single non-patterned wide-field images are more inexpensively obtained since they do not require generation of specialized illumination patterns. In this work, we translated wide-field fluorescence microscopy images to optically-sectioned SIM images by a Pix2Pix conditional generative adversarial network (cGAN). Our model shows the capability of both 2D cross-modality image translation from wide-field images to optical sections, and further demonstrates potential to recover 3D optically-sectioned volumes from wide-field image stacks. The utility of the model was tested on a variety of samples including fluorescent beads and fresh human tissue samples.
In fluorescence microscopy, the quality of the acquired images determines the extent of observable biological phenomena. To address the different noise sources degrading these images, we introduce a model-based framework compatible with several microscopy systems independently from the detector used.
more » « less- Award ID(s):
- 2225990
- PAR ID:
- 10559150
- Publisher / Repository:
- Optica Publishing Group
- Date Published:
- ISBN:
- 978-1-957171-29-6
- Page Range / eLocation ID:
- JM7A.119
- Format(s):
- Medium: X
- Location:
- Tacoma, Washington
- Sponsoring Org:
- National Science Foundation
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