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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.
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Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images
Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.
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- Award ID(s):
- 2225990
- PAR ID:
- 10494721
- Publisher / Repository:
- Science Advances
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 9
- Issue:
- 35
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.adg9245
