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Sparse denoising and adaptive estimation enhances the resolution and contrast of fluorescence emission difference microscopy based on an array detector

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Abstract

An array detector allows a resolution gain for confocal microscopy by combining images sensed by a set of photomultipliers tubes (or sub-detectors). Several methods have been proposed to reconstruct a high-resolution image by linearly combining sub-detector images, especially the fluorescence emission difference (FED) technique. To improve the resolution and contrast of FED microscopy based on an array detector, we propose to associate sparse denoising with spatial adaptive estimation. We show on both calibration slides and real data that our approach applied to the full stack of spatially reassigned detector signals, enables us to achieve a higher reconstruction performance in terms of resolution, image contrast, and noise reduction.

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Supplementary Material (1)

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Supplement 1       Supplementary Figurs S1-S7

Data availability

Data acquisition was performed at the MRic Microscopy Rennes Imaging Center (France) with a confocal (Zeiss LSM 880) with airyscan microscope. The argoslide is the Argo-Z slide SLG-121 (Argolight S.A.). Objective used is the Plan-Apochromat 63${\times }$ × /1.4 Oil DIC M27. For excitation of the slide, the 488 laser beam was used with a BP 495-550 + LP 570 emission filter set. ERM-1 (aka ezrin in mammals) is an actin crosslinker enriched in the microvilli that has been endogenously tagged with Neongreen fluorophore by CRISPR-Cas9 [9]. The localization of ERM-1 in microvilli patterns in worms has been characterized in Ref. [10]. The software can process 2D and 3D images. A python library and a napari plugin can be found at [11]. The results presented in this Letter may be obtained from the authors upon reasonable request.

9. A. Bidaud-Meynard, O. Nicolle, M. Heck, Y. Le Cunff, and G. Michaux, Development 146, dev174508 (2019). [CrossRef]  

10. A. Bidaud-Meynard, F. Demouchy, O. Nicolle, A. Pacquelet, S. Kumar Suman, C. N. Plancke, F. B. Robin, and G. Michaux, Development 148, 448317 (2021). [CrossRef]  

11. A Python library and a Napari plugin can be found at https://github.com/sylvainprigent/sairyscan and https://github.com/sylvainprigent/napari-sairyscan.

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Figures (4)

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Equations (8)

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