# AI platform for microscopy image restoration and virtual staining

> **NIH NIH U44** · SVISION, LLC. · 2020 · $114,156

## Abstract

AI Platform for Microscopy Image Restoration and Virtual Staining
Project Summary:
 Fluorescence microscopy has enabled many major discoveries in biomedical sciences. Despite the
rapid advancements in optics, lasers, probes, cameras and novel techniques, major factors such as spatial
and temporal resolution, light exposure, signal-to-noise, depth of penetration and probe spectra continue
to limit the types of experiments that are possible. Deep learning (DL) algorithms are well suited for
image-based problems like SNR/super-resolution restoration and virtual staining, which have great
enabling potentials for microscopy experiments. Previously impossible experiments could be realized such
as achieving high signal-to-noise and/or spatial-temporal resolution without photobleaching/phototoxicity;
simultaneously observing many image channels without interfering with native processes, etc. This could
pave the way for a quantum leap forward in microscopy-based discoveries that elucidate biological
functions and the mechanisms of disorders, and enable new diagnostics and therapies for human diseases.
 However, these new methods have not been widely translated to new microscopy experiments. The
delay is due to several practical hurdles and challenges such as required expertise, computing and trust. In
order to accelerate the adoption of DL in microscopy, novel AI platform tailored for biologists are needed
for training, applying and validating DL models and outputs.
 The present project aims to develop an AI platform for microscopy image restoration and virtual
staining called AI for Restoring and Staining (AIRS) platform. With our collaborator, Dr. Hari Shroff
(National Institute of Biomedical Imaging and Bioengineering) we have successfully created DL models for
SNR restoration, super-resolution restoration and virtual staining for a variety of imaging conditions and
organelles in our preliminary studies. The AIRS platform intends to (1)provide a comprehensive suite of
validated DL models for microscopy restoration and virtual staining applications including SNR
restoration, super-resolution restoration, spatial deconvolution, spectral unmixing, prediction of 3d from
2d images, organelle virtual staining and analysis; (2)provide plug and play for common microscopy
experiments; (3)provide semi-automatic update training to tailor DL models to match advanced
microscopy experiments; (4)provide user friendly support for new DL model training for pioneering
microscopy experiments; (5)provide confidence scores to assess the output results by a DL model, (6)
provide DL models that avoid image artifact (hallucination) and allow continuous learning and evolution;
(7) and be able to access the required computing infrastructure and database connection.

## Key facts

- **NIH application ID:** 10328064
- **Project number:** 6U44GM136091-02
- **Recipient organization:** SVISION, LLC.
- **Principal Investigator:** Shih-Jong J Lee
- **Activity code:** U44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $114,156
- **Award type:** 6
- **Project period:** 2020-04-01 → 2021-03-12

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10328064

## Citation

> US National Institutes of Health, RePORTER application 10328064, AI platform for microscopy image restoration and virtual staining (6U44GM136091-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10328064. Licensed CC0.

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