Super-resolution in imaging through a transparent spherical microlens has attracted lots of attention because of recent promising experimental results with remarkable resolution improvement. To provide physical insight for this super-resolution phenomenon, previous studies adopted a phenomenological explanation mainly based on the super-focusing effect of a photonic nanojet, while a direct imaging calculation with classical imaging theory has rarely been studied. Here we theoretically model the imaging process through a microlens with vectorial electromagnetic analysis, and then exclude the previously plausible explanation of super-resolution based on the super-focusing effect. The results showed that, in the context of classical imaging theory subject to the two-point resolution criterion, a microlens with a perfect spherical shape cannot achieve the experimentally verified sub-100 nm resolution. Therefore, there must be some other physical mechanisms that contribute to the reported ultrahigh resolution but have not been revealed in theory.
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http://dx.doi.org/10.1364/OL.38.002988 | DOI Listing |
Ann Hematol
January 2025
Department of Hematology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
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The Azrieli Faculty of Medicine in the Galilee, Bar-Ilan University, Zefat, Israel.
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View Article and Find Full Text PDFCureus
December 2024
Department of Medicine, Ministry of National Guard-Health Affairs, Riyadh, SAU.
Background Thyroid nodules are typically an initial sign of thyroid cancer (TC) and require evaluation by thyroid ultrasonography. Additional measures, such as fine needle aspiration, may be necessary depending on the level of malignancy risk. This study aims to comprehensively analyze TC clinical, radiological, and histopathological characteristics in a cohort of Saudi patients.
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January 2025
Microbiology and Virology Unit, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy.
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Sci Rep
January 2025
School of Computer Science, Hunan University of Technology, Tianyuan District, Zhuzhou, 412007, China.
The railway track extraction using unmanned aerial vehicle (UAV) aerial images suffers from issues such as low extraction accuracy and high time consumption. In response to these problems, this paper presents a lightweight algorithm DA-DeepLabv3 + based on densely connected and attention mechanisms. Firstly, the lightweight MobileNetV2 network is employed to replace the Xception feature extraction network, thereby reducing the number of model parameters.
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