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. 2022 Oct 31;19(11):1427–1437. doi: 10.1038/s41592-022-01652-7

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© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 5 — a) Membrane and b) nuclei results, comparing raw input, dual-view joint deconvolution ground truth, single-view RLD, and predictions from RLN with single-view input under conventional testing (trained on similar images to test data) vs. generalization (models trained with mixed phantoms of dots, solid spheres, and ellipsoidal surfaces). Outputs of single-view RLD, conventional testing, and generalization show close visual resemblance to each other. Quantitative assessments show that the conventional testing results (SSIM-membrane: 0.80, SSIM-nuclei: 0.85, PSNR-membrane: 28.9, PSNR-nuclei: 27.0) is closest to the dual-view joint deconvolution ground truth, while the generalization results (SSIM-membrane: 0.75, SSIM-nuclei: 0.76, PSNR-membrane: 27.3; PSNR-nuclei: 26.1) compare favorably against single-view RLD (SSIM-membrane: 0.74, SSIM-nuclei: 0.73, PSNR-membrane: 27.1, PSNR-nuclei: 25.9). Scale bars: 10 μm.