ArticleNature methods2025
Ring deconvolution microscopy: exploiting symmetry for efficient spatially varying aberration correction.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Video-rate label-free molecular mapping in living plant tissue with a deployable optical encoder.PNAS nexus · 2026Article
- Spiral scan and cylindrical deconvolution to maximize image volume and contrast of multiphoton GRIN microendoscopy.Biomedical optics express · 2026Article
- Chromatix: a differentiable, GPU-accelerated wave-optics library.Nature methods · 2026Article
- A fast open-source wave optics simulator.Nature methods · 2026Article
- An end-to-end hybrid deep-learning approach for single-shot wavefront sensing and correction.Nature communications · 2026Article
- Chromatix: a differentiable, GPU-accelerated wave-optics library.bioRxiv : the preprint server for biology · 2026Article
- Enhancing volumetric microscopy with blind computational correction of spatially variant aberrations using neural field representation.bioRxiv : the preprint server for biology · 2026Article
- System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring.Nature communications · 2025Article
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Authors and funding
10 authors.
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Abstract
The most ubiquitous form of aberration correction for microscopy is deconvolution; however, deconvolution relies on the assumption that the system's point spread function is the same across the entire field of view. This assumption is often inadequate, but space-variant deblurring techniques generally require impractical amounts of calibration and computation. We present an imaging pipeline that leverages symmetry to provide simple and fast spatially varying deblurring. Our ring deconvolution microscopy method utilizes the rotational symmetry of most microscopes and cameras, and naturally extends to sheet deconvolution in the case of lateral symmetry. We derive theory and algorithms for ring deconvolution microscopy and propose a neural network based on Seidel aberration coefficients as a fast alternative. We demonstrate improvements in speed and image quality as compared to standard deconvolution and existing spatially varying deblurring across a diverse range of microscope modalities, including miniature microscopy, multicolor fluorescence microscopy, multimode fiber micro-endoscopy and light-sheet fluorescence microscopy. Our approach enables near-isotropic, subcellular resolution in each of these applications.
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